Aula_05_RonaldoSilva.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aula_05_RonaldoSilva.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Zi1mIXAiIRK"
      },
      "source": [
        "import pandas as pd\n",
        "# Bibliotecas dos gráficos\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# url com os dados para o exercício\n",
        "url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n",
        "\n",
        "# Conecta com os dados e cria uma variável com os dados para trabalho.\n",
        "dados = pd.read_csv(url_dados, compression = 'zip')\n",
        "\n",
        "# exibe o head e o tail do arquivos pandas.\n",
        "dados\n",
        "\n",
        "sns.set()\n",
        "dados.rename(columns={'droga': 'composto'}, inplace=True)\n",
        "dados.head()\n",
        "# Aula 04\n",
        "dados_resultados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true')\n",
        "dados_resultados['n_moa'] = dados_resultados.drop('id', axis=1).sum(axis=1)\n",
        "dados_resultados['ativo_moa'] = (dados_resultados['n_moa'] != 0)\n",
        "dados_combinados = pd.merge(dados, dados_resultados[['id', 'n_moa', 'ativo_moa']], on='id')\n",
        "# dados_combinados['eh_controle'] = (dados_combinados['tratamento'] == 'com_controle')\n",
        "# dados_combinados['eh_24horas'] = (dados_combinados['tempo'] == 24)\n",
        "# dados_combinados['eh_48horas'] = (dados_combinados['tempo'] == 48)\n",
        "# dados_combinados['eh_72horas'] = (dados_combinados['tempo'] == 72)\n",
        "# dados_combinados.head()"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P1f-0B9QsTd"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa']\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O5VjzNRgkeE-",
        "outputId": "0d90da22-59f6-4fd9-e1e7-d6a5b709591f"
      },
      "source": [
        "modelo_rlogistica = LogisticRegression(max_iter=1000)\n",
        "modelo_rlogistica.fit(x_treino, y_treino)\n",
        "modelo_rlogistica.score(x_teste, y_teste)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6267058576527399"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71qaHM3dPyfA",
        "outputId": "5853a206-e8f6-449c-dbc4-ccaba4546c8e"
      },
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "accuracy_score(y_teste, previsao_dummy)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6067604450976275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCNtykmaTQYB",
        "outputId": "b511ceef-a6e0-4e0c-ade5-2ec1d0eff133"
      },
      "source": [
        "dados_combinados['ativo_moa'].value_counts(normalize=True)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     0.60666\n",
              "False    0.39334\n",
              "Name: ativo_moa, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RsM-tSVTqPS",
        "outputId": "751d4b29-6807-41f6-ed56-98c8ef52f57d"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa']\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "modelo_arvore = DecisionTreeClassifier(max_depth = 3)\n",
        "modelo_arvore.fit(x_treino, y_treino)\n",
        "modelo_arvore.score(x_teste, y_teste)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6105395758975436"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "GNwPl568abav",
        "outputId": "a0a21ad5-83d1-4762-dd85-f76f52830fbb"
      },
      "source": [
        "from sklearn import tree\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 10), facecolor='k')\n",
        "tree.plot_tree(modelo_arvore,\n",
        "               ax=ax,\n",
        "               fontsize=10,\n",
        "               rounded=True,\n",
        "               filled=True,\n",
        "               feature_names=x_treino.columns,\n",
        "               class_names=['Não Ativado', 'Ativado'])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v2PDjfOo2f52D2zcweOZyDAYD8bG30mXOyOK+95O3cHEObFtP/Zfbc2D7BhIT4oi7dZPoyGt4+vpbxnn65CY68pple8HkYRiNRirWbkSTtl1UQImICKBiSkQkxwkILMaymRNZMXsKZWrUpWjZygCcOLCLdUvmkJyUSHxMNP4Fi1iKqb//myewGHkKFsYjly8A3nnyERVxFSdXN7x8/SlcphIAVRu8wKafFtKQf4qps8cOcfX8aSb3vb3PlJJKYKnyOLq4YmvvwMLJwylToy5lq9dNlzkji/veT8vOfVj8+VjC1v5EkXKV8fT2w2A03vecDqFj8PTJTWJ8HLNH92Xn+p+p0ah5lrOIiEj2p2JKRCSH8ctXkNDPFnFk11Z+mTud4hWr0+i1YBZPH0P/aQvx8vXn1wVfkJqcZDnH1s4eAKPRYPkZbr/ilmYy/b1xx30M3LltNpspUakmIQPTzwrYb8oC/tofxv6t69i8chG9xs684/jD6kx5ePvRZcgk4Pa6WQe2rsfZ1R0PHz9OHtxtGXczMpyi5asCt7tUAI7OLlR57nnO/XVYxZSIiAAqpkREcpzo69dwdvOgWoMXcHJxY/vqH0n5X+Hk4u5JUkI8+7euo2KdRhm6btS1K5w5eoBCpSqw5/dVli7V3wJLluOHzz8m4vJ5fAMKkJSYQHTkNTy8fUlOSqRM9WcpXKYiw0NeTHfth9WZuj2LnwdGo5E138+mZtDLAJSqUouVcz4lPub2K4ZH9/5B85BemEypJMTG4OrhhSk1hT/DtlCikhbVFRGR21RMiYjkMJfPnmT5rMkYjAZsbGx5/d0PcXZ1p1bTVnz8zqu45fLO1DTmfvkC2bzye76dPBz/AoV59oXX7jju5pmL9n1GMmfsQFJTUgB4sUMPHJyd+WpEb1KTkzFjplWXvll+xuWzJ7Nn4ypSkhIZ8kYQzzRtSbM3unHi4G5WzpkGBgNFy1bhte4fAODi5kHTtl0Z/157AJ5v1xUXNw+SEhP4fHB3TKmppKWZKFGpBrWatgLg3PHDzBrVh/jYWxwO28yvC77gwxk/Zjm7iIhkH4aAgACztUOIiMiDCQkJIeS9gZyKsbF2lDtcD7/EjGG9GPTlUmtHybZc7QwEmCOp/Yw6XyIi2cX9v3UrIiIiIiIid6ViSkREssw7d151pUREJMdRMSUiIiIiIpIJKqZEREREREQyQbP5iYjkMH1bPsPEZX881nteD7/ER11b4ZevIAOnLyb84lm++Tj0n+NXLtHszW481/INy771S+exfNYkPl60EVcPL+JjbvHt5GFEXrmIrb097d8fQUBgUcv4NJOJ8b3a4eHjxzsjPr1vnpOH9rB0xngunzlB8MCxVHq2seXY54O7c/bYQQqXqXTHdeZPHMLJQ3twcnEF4I0+I8lXpCQnDu7iqxHv4+0fAECFWg15vv3b933G5bMmsXPDLzRs9RYNX/1nYWMREcleVEyJiMhj4ZMnn2WtqNz5Ai0/p5lMDH4ziAq1GljGRkVc5djeP/Dyy2PZt+b7WeQtUoIuQydz9cIZfpj+MT3HfmU5/vuKheQuUIjE+Lj/zOLl588bfUeyfmn6BX8bvtKB5KREtq1aku7Yy53ev6Pw+luRspXSFXD3e8aXO/fB3tHpP3OKiMiTTa/5iYhkYyu+nsrmlYss278u+IL1S+aSlBDPpwO78sm7bRjT7VUO/rEx3bknDu7iy2E9LduLP/+YHWtXAHD+xBGm9u/EuJ5tmf5hN6JvRDyyZzi+PwyfPPnIlTvAsu/HGRN4qVNvDP8ad+X8aYpXqA6Af/5C3Ai/zK2o6wBERYTz584tPNOk1QPd0zt3XvIWKo7BYEh3rESlGjg6O2f+ge7ibs8oIiLZnzpTIiLZWOW6TfhxxnjqNm8DwL7Na+n+0efY2tvTecgknFxciY2OYuL7b1GuZv27Fg//nyk1hSVfjKXL0Cm4eeZiz6bV/DznM9r3GXHHuF0bfmH90rnpzvfNU4BOgyc88DPs3bSaKvWet2wf/GMjHj6+5Ctc4o5xeQsX58C29RQtW5mzxw9x49oVbkaG4+7lzY8zxvNSp94kJfx3Vyorfp77Gb8t/IriFavTIuQ97OztAThz9CAfd2+Nh7cvLTu/T56CRe847/8/o4iIPB1UTImIZGP5i5YkJvoG0devERMdhZObG16+/phSU1g591NOHdqLwWi4fTzqOu65fP7zmuEXz3Hl7Cmmf/gOAGlpaXh4pT+vWoMXqNbghSzlT01J4VDYJpqH9AIgOTGBNd/PpsdHX6Qb2/i1jiydMY6xPVoTEFiMfEVKYDQaORy2GVdPLwoUK82Jg7uylOd+WoT0wt3Lh9SUFBZNG8m6H77h+fZvk69IKUbOXYWDkzN/7tzCzJHvM3T2yns+o4iIPD1UTImIZHOV6jRm39Z13IqKpHLdJgDs2vgrsdFRhH66EBtbO4Z1eJ6UlKQ7zjMabTGnpVm2U5OTb/9gNuNfsAh9J6f/PtG/PYzO1JHdW8lfpCTuXt4ARF65yPWrlxjbvTUANyOvMa5nW/pNWYB7Lh/e6DPyfxHNDA9uhrd/PvZuXsPhHZs4smsrKSnJJMbHMXfcIDqEjnmgDA/KI5cvAHb29tQMesnyfau/J6QAKFP9WRZPH0NsdBSuHl53fUYREXl6qJgSEcnmKtcL4rupI4m9dZP3xs0GIDEuFjePXNjY2vHXgV3cuHYl3Xm5cufh6vnTpCQnk5KcyPH9YRQuUxG/fIHERkdx5ugBCpWqgCk1hWuXzqV7de1hdKb2/P4bVeo3tWwHFCrGx4v++X7XsA7P03/awtuz+cXewt7BCVs7O7b/9iNFylXBycWVFiG9aPG/rs+Jg7tYv3SepZD66ZtpFCxelgq1G5BV0Tci8Mjli9ls5uD2jZbfx60bkbh5eWMwGDh7/BBmsxkXd897PqOIiDw9VEyJiGRzeQoWJTEhHk9vP0v3pOpzzZgx/D3GdHuVAsVKkzt/oXTnefn6U6luEB93ewXv3HnJV6QkALZ2dnT6cDxLvhxHQlwsaaZU6r/cPl0xlVVJiQkc27eDNr0GP9D48AtnmD9xCAYM+BcsQvvew//znMtnT1CuZr10+88dP8ysUX2Ij73F4bDN/LrgCz6c8SMAk/uFcO3CWZIS4xnyRhDt3h9OqSq1mDtuELHRUWA2k7dwCdr0vJ1739Z1bP1lMUYbW+ztHQgeONby3bSMPqOIiGQvhoCAALO1Q4iIyIMJCQkh5L2BnIqxsXaUDLkefokZw3ox6Mulj/W+0z/sdtfvXz0Jfl3wBQ6OzpZ1plztDASYI6n9TA0rJxMRkQelqdFFROSRMxptSIiLZWyP1o/1vk9qIbV81iR2bfhVa02JiGRzKqZEROSe/l63Kqu8fP0ZNX+1ZRHbzPpqZG8m9n7zjn071q4g+vo1y/bCKSO4cu5Ulu7z93UXf/5xlq9zNy937sOwr1fy7IuPt7gUEZGHS8WUiIhkC/Gxt7hw4igJ8bFEXrlo2R+29ieir/+zqHC73sPIU7CINSKKiEgOowkoREQEgLB1K9mwdB4YDOQtVIy3+n90x/Ftq5ayfdVSUlNT8Q3Iz1v9RmPv6MS+LWtY9e0MDEYbnFxc6T3+a66cO8mCScMwpaZgTjPTafAE/PIWzFK+A9s2ULZGXdy8vNmz6TeatOnMvi1rOX/iCHPHDcLOwYE+k+bxxZAetOzch/MnjhB55QIvd+4D3O40nT9xhNbdP+Crkb25GRFOSnIS9V9qR+1mr94es2Y5axZ/jZOLG3kLF8fW7vaivNfDL/Ht5OHERd/E1cOL9n1GkMsvT5aeR0REsj8VUyIiwpVzJ1m9aCZ9Js7F1cOLuJjodGMq1m5I7edfAeDnuZ/xx+rl1HupLasWfkX30Z/j6ZOb+NhbAGz9ZQn1X2pHtQYvkJqSQlqaKd31vv44lGsXz6bb/1zLN6nRqHm6/Xs2raJpu7dx9/Rm1kd9adKmM5WebczmlYto2bkPBYqXuWN8hdoNmdSng6WY2rt5DU1e7wxA+/dH4OLmQXJSIhPea0/FOo1ITU3h1wVf0n/aQpxc3Jg2sLNlhsMln39CjYbNqdG4BX+sXs6SLz+h69ApGfgNi4jI00jFlIiI8Nf+XVSq09iy0KyLm0e6MZfPnuTnedNJiIshKSGeUlVqAVC4dEUWTBpKpWeDqFi7IQCFSpVn9aJZ3Iy8RoXaDe7aler4wbgHzncr6joRl85TpEwlDAYDNja2XD57koDAe0/X7uaZCx//vJw5ehC/vAUIv3CGwmUqArBpxUIObL+9nlVURDjXLp3nVlQkRctXxc0zFwCV6zbh2qVzAJw5dpDOQyYCUL3hC6z4WoWUiIiomBIRkQe0YNJQugydTL7CJdixdgUnDu4GoE3PwZw9dog/d25hXM+2hH76HVWfa0bBEuX4c9cWvhz6Lq/3HEKJitXvuF5GOlP7Nq8hPjaG4cHNAEiMj2PP76sICO5538yV6zVl35Y15M4XSIVaDTAYDJw4uIvj+8LoO2ku9o5OTA3tRGpKUhZ+MyIiklOpmBIREYpXrMbMUX1o0OpNXNw9iYuJTtedSkqIxyOXL6bUFHZv/BUPbz8AIi5fILBkOQJLluPI7q1ERVwlIS4Wnzz5qP9SO6KuXeXymb/SFVMZ6Uzt2bSK7qOnU6hUBQAir15i+gdv0zy4J45OLiQmxN/1vAq1GrBm0Swu+vnTouN7ACTExeLs6o69oxNXL5zh7LFDAASWLMfSGeOJu3UTR2cX9m1ZS97CxQEoVKoCezatpnrDF9m18VeKlKn0wNlFROTppWJKRETIU7AoTV7vzNTQThiMNuQrUoI3+466Y8wLb3ZnQu83cPXwIrBEORIT4gBYPnsyEZfPg9lM8YrVyVu4BGt/+IZd63/GxtYWdy8fgtp0ynS26+GXuHHtCoEly1v2+fjnxdHFlbPHDlGjcQu+/3S0ZQKKf3N2cyd3gUJcPX+awBLlAChVpTZbf13C6K4t8ctXkMCSt/d75PKlWfu3mdjnLZxc3MhXuITlOq91G8CCycNYv2SuZQIKERERQ0BAgNnaIURE5MGEhIQQ8t5ATsXYWDuKPGSudgYCzJHUfqaGtaOIiMgD0jpTIiIiIiIimaBiSkQkG0lJScFg1gsFTyOj8fb/viIikn2omBIRyUbOnDmDh4MBG4O1k8jDlssBTp06ae0YIiKSAfrOlIhINmIwGJg4aTL1Gj/P+QQ7kk36Jzy7szHcLqQ8ieWl5i9y4cIFa0cSEZEHpGJKRCSbMRgMhA4YSMNGjfDy8rrnOBdnF5ycnYiNjSMxMeExJhQAWxtb3NzdMJvNxMTEYDKZ7jouKSmZU6dOMuTDQZw/f/4xpxQRkaxQMSUi8pSpWLEiEyZM4OLFi3zwwQdcuXLF2pFyLKPRSKdOnejVqxefffYZs2bNumdRJSIi2Y+KKRGRp4SjoyP9+/enVatWDB8+nBUrVlg7kvxPgQIFGD9+PK6urvTr14+jR49aO5KIiDwENm5ubsOtHUJERLKmVq1azJ8/n5iYGEJCQti/f7+1I8m/REdHs2TJEsxmM1OnTsXZ2Zk9e/aoSyUiks2pMyUiko25ubkxePBgGjRowKBBg1i7dq21I8l/8Pf3Z8yYMQQGBtKvXz/27t1r7UgiIpJJ6kyJiGRTjRs3Zt68eZw+fZrOnTvr1bFsIjY2lhUrVhAVFcWkSZPw9/dn586dpKamWjuaiIhkkDpTIiLZTK5cuRg5ciQVK1YkNDSU7du3WzuSZJKXlxcjRoygatWqhIaGsnXrVmtHEhGRDFBnSkQkG3n55Zf5+uuv2bVrF++88w5nzpyxdiTJgsTERFatWsWZM2cYP348xYsXJywsjKSkJGtHExGRB2C0dgAREflvAQEBzJ07lx49ehAcHMzo0aNJTEy0dix5SDZs2ECDBg1ISUlhw4YNBAUFWTuSiIg8AL3mJyLyBDMYDLRv357Q0FC+/vprpk+fTkpKirVjySNUo0YNJkyYwOHDhxkyZAiRkZHWjiQiIvegzpSIyBOqUKFCLF68mNdff51XX32VKVOmqJDKAcLCwmjcuDEXLlxg3bp1tGrVytqRRETkHtSZEhF5wtjY2Kf6mSUAACAASURBVNC5c2feffddpk2bxuzZs0lLS7N2LLGC8uXLM3HiRK5cucLAgQO5fPmytSOJiMi/aAIKEZEnSKlSpfjmm2/ImzcvwcHBbNy4EbNZn3nlVOHh4Xz33Xfky5ePSZMmER8fz8GDB60dS0RE/kedKRGRJ4C9vT29evXizTff5OOPP2bRokXWjiRPmGLFijFhwgRSU1Pp378/p0+ftnYkEZEcT50pERErq1y5MvPmzcPW1pbg4GDCwsKsHUmeQDdu3GDx4sW4uLgwdepUDAYDe/fuVedSRMSK1JkSEbESJycnBgwYQIsWLRg2bBgrV660diTJJvLnz8+4cePw9PSkb9++HDlyxNqRRERyJHWmRESsoE6dOixYsICoqChCQkI4cOCAtSNJNnLr1i2WLl1KSkoKU6dOxd3dnd27d2MymawdTUQkR1FnSkTkMXJ3d2fIkCHUq1ePgQMHsmHDBmtHkmzOz8+Pjz76iGLFitGvXz92795t7UgiIjmGOlMiIo9JUFAQ8+bN48SJE3Tu3Jnjx49bO5I8BeLi4li5ciURERFMmjSJvHnzsnPnTq1JJiLyGKgzJSLyiPn4+DBq1CjKli1Lv379NMGEPDJeXl4MHTqUmjVrMmDAADZv3mztSCIiTzV1pkREHqFXXnmF2bNns337drp37865c+esHUmeYomJiaxevZqTJ08ybtw4SpcuzY4dO0hKSrJ2NBGRp5LR2gFERJ5GAQEBzJs3j3feeceydlRiYqK1Y0kOsWnTJho2bEh8fDzr16/n+eeft3YkEZGnkl7zExF5iAwGA2+99RZ9+/Zl1qxZfP7556Smplo7luRg1apVY8KECRw/fpwPP/yQiIgIa0cSEXlqqDMlIvKQFC5cmCVLltCqVStatWrFtGnTVEiJ1e3atYugoCBOnz7NunXreO2116wdSUTkqaHOlIhIFtnY2PD222/TrVs3Jk+ezJw5c0hLS7N2LJF0ypYty8SJE4mMjGTAgAFcvHjR2pFERLI1TUAhIpIFZcqUYc6cOfj5+RESEsKmTZswm/UZlTyZrl27xqJFi/D392fy5MkkJiZqwWgRkSxQZ0pEJBMcHBzo3bs37dq146OPPmLx4sXWjiSSIUWKFGHChAkA9OvXj1OnTlk5kYhI9qPOlIhIBlWtWpX58+cDEBwczM6dO62cSCTjoqKiWLx4MY6OjkybNg1bW1v27t2rV1RFRDJAnSkRkQfk7OzMwIEDeeGFFxgyZAi//vqrtSOJPBR58+blk08+wdfXlz59+vDnn39aO5KISLagzpSIyAOoW7cu8+fPJyIigo4dO3Lo0CFrRxJ5aGJiYvjxxx9JSkpi6tSpeHp6snv3bs1GKSLyH9SZEhG5D09PT4YOHUqtWrUYMGAAmzZtsnYkkUfK19eX0aNHU6pUKfr27cuuXbusHUlE5ImlzpSIyD00a9aMOXPmcOTIEbp27cqJEyesHUnkkYuPj+fnn3/m6tWrTJw4kQIFChAWFkZKSoq1o4mIPHG0aK+IyP/j6+vLV199xYABA3jnnXcYMmQIcXFx1o4l8litWrWKhg0b4uTkxIYNG6hfv761I4mIPHHUmRIR+ZfWrVsza9YstmzZQo8ePTh//ry1I4lYTVJSEmvWrOH48eN88sknlCtXjrCwMBITE60dTUTkiaDOlIgIkC9fPr799ls6depEu3btGDt2LElJSdaOJfJE2LJlCw0bNiQ6Opr169fz4osvWjuSiMgTQRNQiEiOZjAYCA4Opk+fPsyYMYMvv/xSM5iJ3EfVqlWZMGECJ0+eZNCgQVy7ds3akURErEadKRHJsYoWLcqyZcto0aIFL7/8Mp999pkKKZH/sHv3boKCgjh+/Dhr167l9ddft3YkERGrUWdKRHIcW1tbunXrRteuXZkwYQLz5s3DbNY/hSIZVbp0aSZMmEB0dDShoaFcuHDB2pFERB4rTUAhIjlK2bJlmTt3Ll5eXgQHB7NlyxZrRxLJtiIiIli0aBE+Pj5MmTKF5ORkDhw4oA8nRCTHUGdKRHIER0dHevfuTZs2bRg9ejRLliyxdiSRp0rhwoUZP348dnZ29O3bV+uyiUiOoM6UiDz1qlevzvz580lNTSU4OJjdu3dbO5LIUycqKooffvgBOzs7pk6dioODA3v27CEtLc3a0UREHhl1pkTkqeXi4sKgQYNo0qQJQ4YMYdWqVdaOJJIjBAQEMHbsWPLkyUPfvn05ePCgtSOJiDwS6kyJyFPpueeeY/78+Vy6dIlOnTpx+PBha0cSyTFiYmJYtmwZsbGxTJ06FW9vb3bt2qXZMkXkqaPOlIg8Vby8vBg+fDjVq1cnNDRUE0yIWJm3tzejRo2ifPny9OvXjx07dlg7kojIQ6POlIg8NV588UXmzJnDwYMH6dq1K6dOnbJ2JJEcLyEhgV9++YWLFy8yceJEChUqRFhYGMnJydaOJiKSZVq0V0Syvdy5czNr1iz69u1Lly5dGDZsGPHx8daOJSL/smbNGho0aIDRaGTDhg00bNjQ2pFERLJMnSkRydbatGnDzJkz2bBhA++++y4XL160diQRuYekpCTWrVvHkSNHGDNmDBUrVmTHjh0kJiZaO5qISKaoMyUi2VKBAgVYtGgRb731Fm3atGH8+PF6bUgkm9i2bRuNGjUiMjKSDRs20KJFC2tHEhHJFE1AISLZitFopGPHjrz33ntMnz6dmTNnYjKZrB1LRDKpUqVKTJw4kbNnzzJo0CCuXr1q7UgiIg9MnSkRyTaKFSvG8uXLadq0KS+99BJffvmlCimRbG7fvn00bdqUw4cPs2bNGtq1a2ftSCIiD0ydKRF54tnZ2dGjRw86duzI+PHjWbBgAWaz/ukSedqULFmSCRMmEB8fT//+/Tl37py1I4mI3JcmoBCRJ1qFChWYO3cubm5uBAcHs23bNmtHEpFHJDIykkWLFuHl5cWUKVMwmUzs27dPH56IyBNLnSkReSI5OjrSt29fXnvtNUaMGMGyZcusHUlEHqPAwEDGjx+Po6Mj/fr14/jx49aOJCKSjjpTIvLEqVmzJgsWLCA+Pp6QkBD27t1r7Ugi8pjdvHmTJUuWYDAYmDp1Ko6OjuzZs4e0tDRrRxMRsVBnSkSeGK6urnz44Yc0btyYDz74gLVr11o7kog8AfLkycPHH39Mvnz56NevH/v377d2JBERQJ0pEXlCNGzYkPnz53Pu3Dk6d+7M0aNHrR1JRJ4QsbGxLF++nJs3bzJlyhRy585NWFgYqamp1o4mIjmcOlMiYlW5cuVixIgRVK5cmdDQUE0wISL35eXlxciRI6lcuTL9+/dn+/bt1o4kIjmYOlMiYjUtWrTgm2++Yc+ePbzzzjucPn3a2pFE5AmXmJjIqlWrOH/+PBMmTKBo0aKEhYWRnJxs7WgikgNp0V4Reez8/f355ptv6N27Nx07dmTkyJEkJCRYO5aIZCPr1q2jQYMGmM1mNmzYQOPGja0dSURyIL3mJyKPVbt27Rg4cCBz5szhs88+06fJIpJlzzzzDOPHj+fAgQMMGTKEGzduWDuSiOQQ6kyJyGMRGBjI4sWLadeuHa1bt2bSpEkqpETkofjjjz9o1KgRly9fZv369bRs2dLakUQkh1BnSkQeKaPRSJcuXXj33Xf57LPPmDlzptaJEZFHpnz58kycOJHLly/zwQcfcPnyZWtHEpGnmCagEJFHpkSJEsyZM4cCBQoQEhLC+vXrMZv1+Y2IPDrh4eEsWrSI/PnzM3HiRGJjYzl06JC1Y4nIU0qdKRF56Ozs7OjZsyfBwcF88sknLFy4UEWUiDx2xYsXZ+LEiSQmJhIaGsqZM2esHUlEnjLqTInIQ1WxYkXmz5+Pk5MTwcHBWgNGRKzm+vXrfP/997i7uzN16lTMZjP79u3Thzsi8tCoMyUiD4WTkxP9+/enZcuWDBs2jJ9++snakURELAoUKMC4ceNwd3enX79+HDlyxNqRROQpoM6UiGRZ7dq1mT9/Prdu3SI4OJgDBw5YO5KIyB2io6NZsmQJqampTJ06FTc3N3bv3o3JZLJ2NBHJxjQ1uohkSJUqVWjRogUA7u7ujBs3jilTpjBs2DB69uxJVFSUlROKiNzb999/T1BQECVLlmT16tVUqVIFAB8fH3r27GnldCKS3agzJSIPzNbWlm+//ZaNGzdSuHBh5s+fz6lTp+jcuTPHjh2zdjwRkQcSFxfHTz/9xPXr15k8eTIBAQFs376dcePGcfToUS5cuGDtiCKSTeg7UyLywLp06ULjxo2JjIykfPny9O/fnz/++MPasUREMs3Ly4vhw4dTrVo1li5dSrNmzQgKCtLrfyLyQNSZEpEH4uPjw6xZs/D29ubIkSPMnDmT6OhowsPDrR1NRCTTnJyciI2N5eTJk4SEhODk5ISNjQ27du2ydjQRyQbUmRKRB7Jw4ULq1atHcnIyly5d4sqVK6xdu5avvvrK2tFERDKtVKlSDBw4kDx58hAQEICnpycmk4mCBQtaO5qIZAMqpkTkgXh4eGBvb09ERIS1o4iIPDJOTk54e3tz8eJFa0cRkWzA1toBRP6tTJkytGz1CgH5AzEYDNaOI/+TlmbiwtnTfL/oOxVTIvJU8Pf3580336JosWLY2tpZO45kQUpKEsePHeObb77h5s2b1o4jOYw6U/LEqFy5MnMXfMfq82auxqWRpv9nPjFsDJDf3Uid3Km81vIlTp06Ze1IIiKZFhAQwIqVKzE7epGYZtTfm2zOaABnGxMx1y7S8uWXVFDJY6ViSp4INjY2HDj8J5/uS2XPlWRrx5F7aBBoT/OAOJ59poa1o4iIZNovq37DI28xwhP0BsTTJK9zGn/u3kKnkBBrR5EcRIv2yhMhb968JJkMKqSecL+fSyZvHn8cHR2tHUVEJNNKFC9GZKIKqafNjWQbypYpZ+0YksPoO1PyRHB2diY+KfWRXT8p8jyXfxhu2U6JuozPcx3J9UxrAG7sWMrNXcvAYMS1+DP4BXXDnJrC1ZUTSLx8DAxG/J7vhUuhSo8s49+uLB9L7F/bsXHxonCPuXcdc33rd9w6tBYAc5qJ5IhzFAv9CRtnd05Obo2NvRMYbTAYbQh8eyYAERu/JnrPz9i4eALg27ALrsWfyVC2NDMkJqfg5OREYmJiFp5SRMR6HOztMZlTrHLvWeOHsn/HJtw9czFm9jLL/thb0Xw+qj+R4ZfxyR1Aj6ETcHFzx2w28+30TzgQtgV7B0e6hI4isHjpR55z5cJZbF61DKPRyBvvDqRctdrpxnz0XgcSE+IBuHXzBoVLlOW9UVPZu20jS7/5DKPRiNHGhvbdQylerjLnTh5j7pTRJMTHYTQaadG+CzWea/rQMpvMZpyc9GGfPF4qpiRHcPApQKFuXwO3i4+TE1/BrVRdAOLO7CX2+FYCu32N0dae1NgoAG7uWQlAoR5zSY2N4sKC/gR2/QqD8cEauqaEGGyc3DKc1aNiU7yqt+TysjH3HONdpy3eddoCEHN8G1F/LMbG2d1yPH/wVGz/VzT9m9czr+Fdu22GM4mIyMNRp0kLGr3Uhq8++fCO/b98N5vSlWvwYttO/PzdbH7+bjavd32fgzu3cvXiOcbN+5lTRw8yd+pohk1f+ED3Sk1JwZSagoOTc4YyXjp7irCNvzFm9jJuXr/GJ/27Mm7uSow2NneM+3DqPx/4fTr8fSrVeg6A0pVrUKlWfQwGA+dP/cXno/oxds5PODg40nXgR/jnK0hU5DWGdWtD2Wq1cHF1RyS70mt+ku1F/j6X09Pac252Dy79MILr27677/j403uw9wrAztMfgJu7VuBdpz1GW3sAbF29AEiKOItz4cqWfTaOrre7VPeRGhvF9W3fcXp6B24d3pCp53EOrIjR6cH/sMQcWo972UaZupeIiDwcK+bPYECH5ox+rwOfjw7l18Vz7jquZPmquLh7pNu/d/tG6gS1AKBOUAv2brv9N2Tvto3UDmqOwWCgaOkKxMfGcPP6/WdVvXzuNN99MYEBwc25evFchp9l7/aN1HiuKXb29vjmyUfuvAU4fezwPccnxMVyZN9OqtRuAICjk7NlRt7kxAT438/++QPxz3d7/S4vHz/cPXMRczMqw/lEniTqTEm2lnDpKDFHNxHY7WvMaSbOftkJx4Di9z3n1uENuJdraNlOvn6B+HMHiVg/E4OtPX5NuuOUtxSO/kWJPbYN97INSbl1jcQrf5Fy6xpO3Pl6hTktjbhTu4je+zNJEWdxL9eI/G+Mx87DD4Dog2u4sW1Ruhz2ufKS9/VRWXr+tOREYk+GkbtZb8s+A3Bhfl/AgFfVFnhWbWE5FrVzGbcOrMYxoCR+TXpkqnMmIiJ3On3sMLu3rGPUzCWYUlMZ9s7rGX4V71bUDTy9fQHwyOXDragbAERFXsPb198yLpdvbqIir1nG/i0pIZ6wTWvYvOr2q4PPNnmJ0R2W4uTsAsC3n4/j2P5d6e5b47mmvNi20x37oiKvUaRU+X/u6ZObqMjwe2bfs20DpSvVwMnF1bJv99b1LJk1lVs3b9Dno+npzjl17BCpqSn4BeS/53VFsgMVU5KtJZw/hGuJOhjtHABwLZH+ne5/M6emEHt8G76Nuv6zL82EKeEWBbt8SeKlo1xePIzCvb/Ho1IzkiLOcfarrth55MYpfxkMBpt017y0aBCJV07g3yIUl6LV062P5VE+CI/yQQ/hadOL/WsbTvnL3fGKX4FO07Fz9739auK8Ptj7FMA5sCJe1V7Gp14HwEDkhtlcWz2dPC8PfCS5RERykhN/7qNSrfrY2zuAvQMVa9bL0vUMBsPtT8YyoFfrhuQvXJyOfYcTUKBQuuPtu4dmKdP97NiwinrNWt2xr2qdhlSt05BjB3ezdM5nDBg/03Ls5vUIvvp4EF0GjMb4gK/OizypVEzJUyklOpyLCz8AwLPqS3hVewmA2JM7cMhTDFvXXJaxdu6+uJWui8FgwClfaTAYMcVHY+viSe7ne1rGnZvVDXvv9J+g+TZ6m5t7VhL+6xRcilTDo9LzOOUtZTn+KDtTtw7d2WX7+3ng9quJrqWeJeHSUZwDK97xzB5VXuTiQhVSIiKP0vVrV5ky+Pbfkeeav0aD5q3vOdbdKxc3r0fg6e3LzesRuHve/jfby8eP6xFXLeNuRITj5eOX7vx3h01k86plfDrsfWo815Q6TVrgkzvAcjwjnSkvHz9u/PuekeF4+eS+a+6Y6ChOHztMr5FT7nq8ZPmqzLoyhJjoKNw8vEiIi2XSoB682rEnRUtXuOfvQyS7UDEl2ZpTgXJcXTkB72fbQ5qJ2L+241mlOXYeuS0TTvzbrUPrcS935/eLXEs+S/yZfbgUqkxy5AXMphRsnD1IS04EzBjtnYg7tQuMNjj4Baa7poNfIXI/3wu/xt2IObaZiPUzMcXewC+oOy5Fqz+yzpQpMZb4c/sJeGWwZV9acgJmsxkbB2fSkhOIP7UL73rBAKTGRGLr5gNA7NEtOPil/+RSREQyrliZSsyZPJIX23UmzWRi/47N1H/xFbz9/Bn11Q8PdI1Kteqzdc1PvNi2E1vX/ETl/03mUKlWfdYt/46azz3PqaMHcXJxS/eKH0C5qrUoV7UWsdE32b7uZ6YOeQ9XD0869h2Or3/eDHWmKtWqz5cfDaTpq29x8/o1wi+do3DJsncdu2vzWirWrHu7K/c/4ZfO4xeQH4PBwNm/jpCSnIKruyepKSlMG9ab2kHNqVbv0byxIfK4qZiSbM0pbylcS9TmzBch2LrkwsGvMDYOrncdm5acQNyp3fg373fHfs9KzbiyYiynp3fAYGNLnpaDMBgMpMRFcXF+PzAYsHX3JaDV4Lte928GWzvcyza8/R2rm1cxxUdn6pku/TCC+LP7MMVHc3LiK/jUD8GzyotE7VoBYOmyxRzdgkuRahjtnSznpsZGcWnR7RmizGkm3Ms1wrXY7QV2r635kqSrJ8BgwM7TP93vQUREMqdwybJUqlWfwZ1fxcMrF/kLF8XZ5e5/iz4fHcqxA7uJjb5J79cb0bJDd+o1a8WLbToxfVQ/Nq9ahnfuPPQYMgGACjWe5WDYFvq/+QIOjo507n//NxpcPTwJeuUNgl55g1PHDmE0pn89/b/kCyxK9fpBfNDxZWxsbHiz5yDLTH4TP+hOx77DLd2xsI2/8UKbjnecv3vzOrauXYmtrS129g70GDIOg8FA2O+rOX5wL7G3otm6+icAOoeOomDRkhnOKPKkMAQEBJitHUKkZMmSzPx2Ke//npThc9OS4jE6OJOWnMj5b3ri37wfjgElHkFKAZjX3J3aNaoRFaUZmEQke7pw4QI7Lz/cdaYSE+JxdHImKTGBMe+HEPL+0MeyHpT8w94GCjrEU77c3btoIo+COlOS7V1dOYGkiLOYU5PxqNBUhZSIiDx230waweVzp0lJTqJ2UAsVUiI5hIopyfYCXh1q7QgiIpLDdfvwE2tHEBErUDElOV7Ehtk4F6yAS5Gq9xwTc2wryRFn8X72jSzdK/Hyca4sG0NaajKuxWri93yvdFOpJ0Wc48rysSRd+Qufhp3xrt329v7I81z+YbhlXErUZXye60iuZ1oTsX4Wsce3gsGIjYsneV4ehJ27T5ayiojI4/HjN9MpUb4KZarUvOeYvds3cvnc6XQz72XUmb+OMGvcYJKTkqhQ41na9xiQ7u/Q304fO8yonm/SffAnlgkjvp8xif1hWzCb0yhb5Rna9xhAYkI8Y3oHW867ERFOrUYv0L7HgCxlFckOVExJjufb4L//MLmVrAMl62T5Xld/noh/i1Ac85Xm4oJQ4k6G4Vrszj+eNk7u5G7Wi9ijW+/Y7+BTwDJDoTnNxMmJr+BWqi4AuWq3xbdhZwBu7FjC9U1zNMGEiEg20Sqkx3+OqVzrOcsMf1kxd8poQvoMo0ip8kz8oDsHd26lQo1n041LM5lYPHMyZas+Y9l34s/9/PXnfj6auQSA0e914NiB3ZSqWO2OWQuHvvM6VZ5tmO6aIk8jFVOSY0T+PpdbB9dg4+KJrbsfjgHF8a7dlsvLxuBavBbuZepzcnJrPCo0JfavbWBKJaD1SBx8C3Jz3yoSLx/D/4X3M33/1JhI0pLiccpfBgCPik2IPbolXTFl6+qFrasXsX/9cc9rxZ/eg71XAHae/gDYOLpYjpmTE8nwao8iIvLIrZg/g+3rfsbNMxe5fHMTWLw0zVoHM/OTwVSsWZdq9YLo264ptYOas/+PTZhMqfQYOpGAAoXY8tsKzvz1J2/1GpTp+9+8HkFifKxlfafaQc3Zu23jXYuptcsXUvXZxpw5ftiyz4CBlOQkUlNTMJvNmEypeHh533He1Qtnibl5gxLlqmQ6p0h2omJKcoSES0eJObqJwG5fY04zcfbLTjgGFL/rWBtnDwq9M5uoncu4sX0ReV6692sKcWf2cu23z9LtN9o5ULDzF3fsS7kVia37P2uD2Lr7khITmannuXU4/WK9EetmEn3gN4yOrhQInpqp64qIyKNx+thhdm9Zx6iZSzClpjLsndfvOUmFm4cXI2csZv2KRaxaPIdO/Ubc87pH9+1k4Rfj0+23d3BkyKfz79gXFXkNL99/Ft/N5ZObqMhr6c69ERHOnq0bGDhxNrPH/1NMFS1TgVIVq/Heaw0xY6bRS20IKFj4jnN3bPyN6vWb3PPVQZGnjYopyRESzh/CtUQdjHa3FxV0LVH7nmPdSt9+dc4xoAQxRzff97ouhSrfdXHgR8mcmkLs8W34Nup6x37fRl3wbdSF65sXEBX2I74NOt7jCiIi8rid+HMflWrVv724rb0DFWvWu+fYKnVuf1gWWLw0u7euv+91S1Wq/sALAz+ohZ+Po3WX3hiNxjv2h186z5XzZ5j8/VoAxvXvyvGDeyhR/p8uVNjG3+j6wZiHmkfkSaZiSuT/MdjY/e8HI+Y0033HZqQzZefuQ+qtCMt26q0I7NwyPklE7MkdOOQphq1rrrsedy/fmAvfhqqYEhHJpuzs7QEwGm1IM93/71BGOlNePn5ERYRbtm9EhlsW3/23M3/9yRejb7+VERMdxYGdWzDa2BJ+6RxFSpXH0en/2LvvgKjrP47jzxvAsYcIAooD994rLbfl3unPzCwTNw7ce2fuiYDbXJWalmmaZo5cOUvNiQqigIpsOLi73x8YRVgOlC/j/fgn73vf8boL+N773t/v52MFQMWa9bhx+UJaMXX35lUMBgNFZVh4kYdIMSXyBEvPCjz4di756ncHo4HYa7/gUK11pvf7Mp0pra0zagsrEoIvoStYlqjzP+BYq8NLHzP6twPYVWiSbpn+UTDm+QoBqSMPWjh7vvR+hRBCvDklylVh7YKptPpfb4wGA+dPHKZBq46Z3u/LdKYc8uVHZ2XDjcsX8CpTkWP7vqVp+/9lWG/exr1p//7zfq5q9Rpx8qe9HPp+G60Mn2Aymbh68VeadfhrlNsTB/dQu9G7mX5NQuQkUkyJPMHSoww2pd4iyK8XWmsnLFyKobGwyfIcri2Hcf+bWZiSk7AuUQvrp4NPRJ7eCYBjjbakxDzidkAfjElxoFITeeJrig5Yj0ZnjVGfQNzNXzOM1Be+3x/9o2BUKhVa+wIUaD08y1+bEEKIf1esdHmq1G3A+N6dsHd0olCx4lhZZ/15qKfPOAKfDo1esWY9KtZMHan24LdfAtCodZd/3bbG2025fO4U43p3RIWKCjXeokrdBmnPn/r5B4bNXP5G8wuR3ajc3d1NSocQonTp0gRu3MbQQ0lv7BjGpHjUFlYY9YncXTOIAq190bmXemPHy63Wt7bjrVo1iIyMVDqKEEK8kuDgYE6FJmf5cRMT4tFZWpGUmMDMob3oNXTivw5CIV6euQYKW8RTsUJ5paOIPEQ6UyLPePDtXJIibmNK0WNf6V0ppIQQQmSpNfOnEHrnFsn6JN5q1kYKKSFyASmmV3gwfQAAIABJREFURJ7h3mmi0hGEEELkYf3GzVY6ghDiNVM/fxUhhBBCCCGEEP8knSkhXsKdNYNxadYfS4/SWXbMPyfjNSTGUmrcDxmej758iNCtEyncJyAtV+KDmzz4di7GpDhUKhWF+wSgNrMgeIMvKTGPMBkNWBWuiGvLoajUmix7LUIIIV7erGEf09V7OEVLlcuyY369ajHH9n9LXEw0AbtPpi3fuPxz/jh/GoCkxERinjzGb9cxAD5qWplCRUsA4ORSgKHTlwBw6ewJtvrPx2QyYWFpxacjp+Hq4cmjsPsEzB5PfFwMRoOBLp8OoVKt+ln2GoV4HaSYEiKbsylVF8da7bm5uHuG5wxJ8USe+Bpdwb+uuzcZUri/fRpuHcajK1AcQ3wUKk3qr7p75ylodNaYTCbubZ1AzKVD2FVonGWvRQghRM5Quc47NGnXjZEftkq3vHv/kWn/3r9jE3du/JH22Nzc4pnDtK9bOIMh0xbhXrgYB3ZuYdcXAXw6ajo7NwZQs0EzGrd5n3u3bzJ/7ADmbdqbYXshsjMppkSOZtQncO/LSaRER2AyGXF+50Psyjfm4aG1xF79BWNKEpaFylOgtS8qlYo7awajK1CChLsXMeoTcOswjkdHviAp7BZ25RuRv/Gn6CPvE/LFCHRuJUm8fw0Ll6K4tR+H2lyX7thxN04R8dMaTAY95o4euLUbjdrCivD9K4i9egyVWoO1Vw1cmg/I1Gu0LPTv30Q+PLiSfPW68+jY5r9y3TyNhasXugLFAdBY2ac9p9FZP33jDJgMKaDKVDQhhMiTkhLiWTZtBI8jwjAaDbT9wJtaDd/lm/UrOH/iZ/RJiRQvV5leQyeiUqmYNexjPIuX5tpvZ0lKTKDPqBl8t3kVIUHXqdmgOZ0+HkTEg3vMG92PIiXLcvv6FTwKe9Fn9AwsdJbpjv3br7+wY+1yUpL1uLgXovfIaegsrfgycCHnfjmEWqOhfPU6dOvr++zwL6h42UrPXefEwT2079n/ueupVJAQHwtAfFwsDvnypy5HRWJcHAAJf1suRE4ixZTI0WJvnERr60yhDz4HwJCY+sfasWYHnBt8BEDotunEXvsF21JvAaDSaCniHcjj419xb/NYingHora049aibjjVSZ1fQ//wLgXajsLKswL3v/mMyNM7yPdWt7TjpsQ94eHh9Xj2nI/a3JJHRzby+PiXONZsT+yVIxQd9AUqlQpDQkyGzHFBZwnfuzTDcrWZBYV7+73wa08MvUpyVDg2JeukK6b0j4IBFcHrh5MS/wS78o3JV++vSRmD1w8n4d4VbErUxrZsgxc+nhBCiFQXTx/DIV9+hs1cBkB8bOrf+ibtutHuw74A+M8ay/njP6fNw6TVmjHFbwv7tn3Book+TPHbgrWtPSN6tODdjj0AuB98m499p1CyfBVWzpnIgV1badHlo7TjxkRFsuuLAEbNCcDC0ordm1ez96v1NGnblTNHD/DZ2l2oVCriYqMzZL5y7hSb/OZkWG5uoWPCkg0v/R48DAsl4sE9ylapmbYsWa9nUr+uaDQaWnb9hGr1GgHw8fDJzBszAHMLCyytbJi49AsA2vfsx5xR3uz/ZhNJiQmMnBP40jmEUJoUUyJH07l4Ef7DcsL3+WFTqi5WhVO/SYu7fZbHRzdjSk7EkBCDhUsReFpM2ZROnaDQwrUY5vmLorV1BsDM0Y3kqHDUOhu09i5YeVYAwK5iUyJPboO/FVOJIZfRR9zhzqrUrpPJkIxlwXKoLaxRac15sHM2NiXrYFOybobM1kWrUrTf6ky9bpPRSNgPy3BrN+YZzxlIuHvx6X1SOu6uG4rOvRTWxaoBUOjDeRiTkwjdNo34oLNYe9XIVBYhhMhrChUtwZYV89gasIDKtd+mVMXUv69Xzp/i+61r0CclEhsdjUcRr7Ri6s//FixWAo/CXmldmPxuBXkU8QArG1ucXApQsnwVAOo2acn+7Zvgb8XUzcsXCb1zi+k+PQFISU6meNlKWNrYYGZuwaq5k6hc+20q134nQ+YyVWo+8xK8V3Xy4F5qvN0Uteav+27nbdqLU35XwkNDmO3bm4LFSuDqXogftn3B8FnL8CpTke+3rmGT3xw+8Z3CiYN7qNesLe916cmNSxcImDWWGau2o1bL+Ggi55BiSuRo5s6FKOq9ktjrJ4g4sBLrYtVweqsbYd8toIh3AGb2rkT8tBpjij5tG5XG7Ok/1Ki0Zn/tTKXGZDT8+eAfR0r/2IQJq2LV8eg8KUOmwn38ib91hpjLPxN5ajueHy1K9/zr6EwZ9fHow4O4u9YHAEPsY+5tHoNHt1mY2blgWbgSWmsHAGxK1CYx9FpaMfXnsWxL1yPmj6NSTAkhxEsqUKgIU1Zs5cLJI2xbs5SyVWrRomsv1i+awWS/LeRzKcCOdctJ1v81Eb2ZmTkAKpUa7dN/A6jVaoyG1HOP6p/nHtU/zj0mE+Wq1ab/+M8zZJq0bBOXz53k9OH9/PjNZkbPW5Xu+dfdmTpxaC8fDh6bbplTflcAXNwLUrpSde5ev4KllTV3b17Fq0xFAGo1eJe5o/sB8POeHfh+lnreK16uEsnJScRGRWLnmO+l8wihFCmmRI6WHP0QjaUt9pWaodHZ8OTsd5ieFk4aKweMSfHEXP4Z27IZv6X7LylRYSQE/45lofJE//Yjlk+7VH+yLFiOsN0L0D8KwTxfQYz6BFKiH6K1zYcxOQmbknWw9KzAzYVdM+z7dXSmNDobSoz6Nu3x30cZNHfy4NGxTRj1iag0WuLvnMepTheMSfEY9fFobZ0xGVKIvXYcq8IVM5VDCCHyosiH4Vjb2fNW01ZY29jy8/fb0wonW3sHEhPiOX14PzXebvpS+30Ufp8bly5QvFwljh/4Pq1L9SevshVZv2QmYffu4urhSVJCPJEPw3FwdkGfmEClWvUpUa4yvh+0yLDv19mZCr0bRHxMdLr7quJiojG30GFmbk5MVCTXL52nxfu9sLa1IyEulgfBtylQqAi/nzmOe+GiAORzKcDlsyep/27bp5MZ67F1cHotGYXIKlJMiRwtKfwWEfuWp3aZ1FpcWw1DY2mLQ7VWBC3ridbGCUv3lx/G3NzZk8hTO7j/zWeY5y+CY4126Z7XWjvg1m4MoV9PxWRILd6cG/VGbWFJyOaxqQWdyYTru5kbfAIgfJ8f0b/9iCk5kRvzOmJftSX5G378r+trLG1xqvM+twP6gEqFTYna2JSsQ0rsY0I2jcVk0GMymbAqUgWH6m0znU8IIfKakKDrbPGfj1qtRqPV0tNnPNY2drzTsiNjP+mAvZMzRUuVf+n9uhUqwo87t7By7kQ8ChejUZsu6Z63c3Di05HT8JsximR96rmn48cD0VlZs3CCz9OCzsT/+mVu8AmArf7zOX7we/RJiQx5vwnvtOiQNtjEyZ/2UKvhu6j+1jkLvXuLtQumolKpMZmMtOz6MR5FvADoNXwSS6YMQ6VSY21rxye+UwHo1teX1fOn8MO2DahUKnqPnJZun0LkBCp3d3eT0iGEKF26NIEbtzH0UNLzV37D9JH3Cdk0mmID1ikdJVta39qOt2rVIDIyUukoQgjxSoKDgzkVmqx0jHQiHtxjwbiBzFy1Q+koOZa5BgpbxFOxwssXskK8KrnDTwghhBBCCCFegRRTQvyDuaObdKWEEEJkqfwFPKQrJUQOJMWUEEIIIYQQQrwCKaaEEEIIIYQQ4hXIaH4i17g6ozmlxv2QpcfUR94naGkPzJ0904Y7v//NZ8Re+wWNteMzLxd8dGwLEfuWU3zkLrTWDphMJsL3LCb2+gnUZha4tRuDzr3Ufx434sdAoi7sxZAYm+41R57eyZNT20GtQW1uSYHWI7BwKULczdOE7/cHQzJozHBp1i9t3qngDb6kxDzCZDRgVbgiri2HolJrCP9hOVEX9+FU933y/W3CYiGEENCnZS0Cdp/M0mNGPLjHmF7tcCtUhGkBX3E/OIjl00amPR9+P4QOH/WneccebFuzlLPHfkKtVmP7dBRAR2cXrpw/zaKJPuQv4AFAtXqNafdh37R9GA0GJvXvhmM+F4bNzDgn4t/9cfFXNi37nOBb1+k/fjY13mmW9tzRH3aya2MgAG26f0q95m1JSkxg2VRfwkODUak1VKnzDl0+HQLAwW+/5MDOLajVGiwsreg1dGLaaIAAj8LuM+bjdrTr2Y8WXT5Cn5TItEE9uHfnJou+PICtvWPm32AhXoEUU0JkkpmTR7p5o+wrv4tjzfaE7piZYd3kqDDib55Ga++atizu+gn0j0IoNngTiSGXefDdfIr08f/PY9qUqotjrfbcXNw93XK7Ck1wrJE63HnMH0cJ/2EphXrMRWNlT8H/fYaZnTNJYbcI3uBLcd/tALh3noJGZ43JZOLe1gnEXDqEXYXGuDTvj8pc98rvixBCiNfPxb1g2nxRboWKpv3baDAw5P0mVKvXGIAWXT6iY6+BAOzbvpGdG/z5aOgEAEqWr/qvhdK+7Rtx9yxKQlzcc7Pkc3Gj98jp7PlqbbrlsdFRfLNhBZOXb0GlUjGp3/tUqdsQrZkZ73XuSZkqNUlJTma2b28unDxCpVr1qdOoBY1apw4Ff/aXn9i8Yg6+n61I2+cmvzlUrFkv7bG5hY5pAV8x/H/vvsjbJsQbI5f5iWwpfP8KIk9uT3sc8dNqHh3bjDEpnrtrhxC04hOClvUk5o8jGbaNCzpH8MZRaY8f7F7Ak3N7AEgMvcqd1YMIWtGb4PXDSYl5+NqzWxWpjNrS7pnPhe9dSv5m/dLNah/7x1HsKzdHpVJhWagcxsTY5+ayLFQOra1zhuUanXXav03JiUDqcXRuJTGzS13f3KUoxpQkjH9ObvznNkYDJkPKn5sIIUSe8WXgQn78Zkva4x3rlvP9l2tJTIhntm9vJnp3YVzvDpw99lOGba+cP838sQPTHq9fPJMje3cCEHTtMjOH9mJi3/eZM6ovTx5FvLHXcOncSfK7F8LZ1R0AS2ubtOeSEhNe6G/744gHXDh5mHdadHihY+Yv4IGnV0nUqvQfJ3/79RjlqtbBxs4ea1s7ylWtw8XTR7HQWVKmSk0AtGZmFC5RhsiHYc/O+7fAZ44eJL+bR7pOlRDZhXSmRLZkV74RYXuW4Fgr9Q96zKWfKNRjLiqtOR5dZ6DRWZMS94Q7K/thU6reC03yZzKkEPb9Qjy6zUJr7UD07weIOLASt3aj060XdXEfj49tybC9uZMHHu9Pe+XXFPPHEbS2zugKFE+3PDnmIVo7l7THWrv8JEc/fGax9CIiT27n8fEvMRmS8fxoYcYcl39G51YStdY8bVnw+uEk3LuCTYna2JZt8ErHFUKInKpWg+ZsXP45Tdp1BeDUoX34zl6Bmbk5g6csxNLahpioSKYO/IAqdRu80DknJSWZL5bMwmfaIuwcnDj5016+Xr2E3iOmplvvlx93s+fLtRm2d3EvxKDJ81/4NZz8aS+1G72XbtnXqxZzbP+3WFrbMHreqrTlNy5fYPynnXDIl5+ufYdTsEjqeWnjss/p0mcYifHP70r9l8iH4Ti5FEh77JTflciH4enWiYuN5vyJn2nW4YO0ZT9+s4W9X6/HkJLMqLkrAUhMiGf3ltWMnBPwzPdJCKVJMSWyJZ1bSQxxkSRHP8QQ/wSNzhYze1dMhhQiDgSQcOcCqNSkREdgiH2M1jbfc/epf3iXpPAggtcPS11gNKB5xnb2FZthX7FZhuWZYdQn8ujwFxT6cN5r3e+zONbqgGOtDkRd3M/Dn9fj3mFc2nNJ4UFE7F+RIUehD+dhTE4idNs04oPOYu1V443nFEKI7KJwiTJEP3lM5MNwYqIisbK1I59LAVJSkvlq1WKuXjyDWq0m8mE4UZGPcHB6/pddD4JvE3L7BnNGegNgNBqwd8qfYb26TVpSt0nLTOVPSU7m3C+H6PyJT7rlnT4ZTKdPBvPtppX8+M1mOnw0gCIlyjB/8w/oLK24cPIIiycO4fP133H++M/YOTpRtGRZrpw/nak8z2MwpOA3fRRN2/8PF/eCacubtOtKk3ZdOX5gN7u+CKDP6BnsWLec5p16oLO0eqOZhHhVUkyJbMu2XENiLh/CEPsY2/KNAIi6uB9D3BOKeK9EpdFyY0GXtMvV/qRSa8BkTHtsevq8CRPm+YtS5FO//zzum+hM6SPvkfzkPkF+HwOQEh3Bbf/eFPnUHzNbZ1Ki//rGLiU6Iu2SvMywK9+YsO/++lYzOSqckC3jcOswDnMnjwzrq80ssC1dj5g/jkoxJYTIc2q+3YzTh/cT9fghtRo0B+D4j98TExXJlBVb0GrNGP6/d0nWJ6XbTqPRYPrbOefP500mEx6FvZi49Iv/PO7r6ExdPHWUwiXKYO/07C8W6zZuybyx/enw0YB0l9NVqlWf9YtmEBMVybVL5zn3yyEunjxKsj6JhPg4VswcQ9+xs14ow985Orvwx/lf0x4/jgijdOXqaY/XzJ9KgYKFad6xxzO3r9XwPdYtmgHArSu/8evhH/kyYAHxsTGo1CrMzC1o2k4GRhLZgxRTItuyK9+IB7s+JyU+isK9FgNgTIxFY+2ISqMlLugsKU8eZNjOzMGVpIg7GFP0mJKTiL91BkvPiljk88QQ/4SE4N+xLFQekyEF/aNgLFyKptv+TXSmdK5elBi5K+3xjQVdKNInAK21Azal6xF5cju25RuTGHIZtc467RK/W0s+oNig/z4R/53+UTDm+QoBEHv9OOb5Ur/xMyTEELJxFC5NvLHyrJC2vjEpHqM+Hq2tMyZDCrHXjmNVuOLreMlCCJGj1GzYnDXzphAT9YSxC1IHFUqIi8HOwQmt1owr507xMCw0w3b5XN0JvXOLZL0evT6Ry+dOUrJ8VdwKFSUmKpIbly5QvFwlUlKSeRByJ+2Suj+9js7UiYN7Mlzi9yDkDgUKFgZSB3RwK5R6rnvy+CH2jvlQqVTc/OM3jCYjNnYOdOntQ5feqZ2tK+dPs+fLdWmF1JcrF1GsdHmqPx3c4nkqVH+Lr1ctJi4mGoDfz/xC56f7/nr1EhLiYvh4+OR/zXvhxGFcPTwBGLfor1Fxd6xbjoWllRRSIluRYkpkWxYuRTEmxWNmmz+tuLCr2JSQTWMIWtYTnXtpzJ09M2xnZu+KXbmGBC3riZmjGxZuJQBQac3w6DKVsD2LMCbGYTIacKrdOUMxlVn3vppC/O1zGOKjuDGvI84NeuFQrdW/rm9dojax145za1E31GYWFGg3BoCUuCdgMj1zm/B9fkT/9iOm5ERuzOuIfdWW5G/4MZEntxN36wwqjRaNzha39mMBiDy1Hf3jezz8eR0Pf049MRXqMQ8wEbJpLCaDHpPJhFWRKjhUb/ta3w8hhMgJChYpTmJCHI7OLjjkS70cr06TliwYN4hxvTtQpGQ53Dwzni/yuRSg5jvNGNe7A84FPChcvAyQOsDCwEnz+GLpZyTExWIwGGjesXuGYiqzkhLi+f3M8bSR+v701cqF3A++jUqlxtnVjZ5DUp8/fXg/B3d9iUajwdzCgv7jP3/uPWAht65TtU6DDMtv/fE7iycNIS42mnPHf2b7Oj9mrd6BjZ09bT/wZnL/1KKnbY++2NjZ8zjiAd9uDMTNsyiT+r4PQOO2XWnQsiM/frOZS2dPotVqsbKx49NR01/DuyPEm6dyd3d/9qc1IbJQ6dKlCdy4jaGHkp6/cjaij7xPyKbRz5xPKrNir/6CPjIUp9qdXvu+X1TET6tRm1umm2dqfWs73qpVg8jISMVyCSFEZgQHB3MqNFnpGC8t4sE9FowbyMxVO7L0uHNG9WXE7BXPX1EBw//3LpP9NmNr74i5BgpbxFOxQnmlY4k8RIZGFyITVGo1xsTYtHuhXiebUnUVLaTCf1hO9MX9qM0sFcsghBDiL2q1hoS4WCb06Zylx82OhZQ+KZEJfTqTkpKCSiUfZ4Vy5DI/kas9q7PyOpnZu1J8+LYXXj9k81hSYh9T5NO/TkxPzu3B2qtG2qAT93fOxqnO+1i4FMlUtifn9pAY+gcFWg59pe1dmvfHpXn/TGUQQoi87s/7fFp0+SjT+8rnUoAFW/Znah+LJvgQFfmQiUs3pi07sncn5avXwdE5dZqOVXMn8W6nDzM9r9ORvTsJunaJDwePzdR+nuXPSXuFUJqU8kJkEUNCDImhVzEmxqJ//NdNzFHn96SbpNet7ahMF1JCCCHEP8XFRnP7+mXi42IJDw1JW3503850Ewp/4jtFJsgV4gVJZ0rkGlHn9/L4ly2ACgtXL9w7jk/3/JNfv+XJmW8xGZIxc/LAvcN41OY6oi/9xMNDa1Gp1Kh11hT+eClJ4UHc/2YWJkMKmIx4vD8tbZS8VxVz5TA2pd5Ca+1I9O8HcH67B9GXDpEYepX726ahMrOgcG8/gr8YgUuz/iSGXiX58b207tDfO00hm8eSHBWOKUWPU+1OOFRv83Sd73l05As0OhssXIuj0poBqfd2Pdj5GYb4KDRWDri1G4OZg2umXo8QQoj0ju7bxZ4v16FSqShUrCTeY2ame/7Q7q85tHsbKcnJuHoUos/omVjoLDn18z6+We+HWq3B0tqGcQvXEnL7Bis/n4ghJRmj0cigyfPTRrt7VWeOHKBynXewc8zHyZ/20Lr7p5z+eR9BVy+xYuZozC10TFiygXlj+tPVezhB1y4RHhpMV+/hQPpO06IJPjyKeECyXk+zDt1p2Cr1svTDe7/hu02rsLKxxdOrJFqz1AniIx7cY9WcicREPcHOwZHeI6aRz9UtU69HiOxAiimRKySFB/Hw8HoKf7IcrbUDhvjoDOvYln0bh+qtAYg4EMiTs7txqt2RR4fWUqjHXMzs8mNIiAEg8vROHGt3wr5iM0wpyenmEPnTvS8noX8UnGG5U50u2Fd+N8Py6N8O4NygJ1prJ+5tHY/z2z2wK9eAyFPbcWnWH0uP0v/I+w53VvZLK6Zifj9IvrdT5+RwazsajZUdxuQkbgf0wbbsO5gMyTz8aTVFvFeisbDm7lqftJEMw75fhH3ld7Gv/B5Pzu4mbM8iCnZLf5IXQgjx6kJu32DXF4FMWLIeW3tHYqOjMqxTvV4TGrRMLTq+Xr2Ew3t20LT9/9i5YQW+n63AKb8rcbGp56+fvv2KZh26U7dJS1KSkzEaDRn2t2zaCB4E386wvHmnHtRr1ibD8hMH99C2hzf2jvlYMmUYrbt/So13mvHjzi109R5O0VLl0uet34Rpg3qkFVMnD+2lTfdPAfhkxFRs7OzRJyUyuX83atRvQkpKMjvWLWeK3xasrG2ZNfwTChdPPbd9sWQW9Zq1oV7zthzes4Mvln6Gz7RFL/EOC5E9STElcoW4W2exK9sQrbUDABoruwzrJIUHEXFgJcbEGIz6BKyL1wTA0rMC93fMwq5cQ2zLvp26rFA5Hh3eQEpUBLZl335mV8qjy5QXzpcS+xj94xAsPSumDkGr0ZIUdgsL12L/uo3W2gEzRzcSgi9hnq8g+od3sHw6R9Tjk18Te+VI6r6jwtE/CiEl9jFWRaqkvQe25RulFXuJIZco2DV1mFn7Ss2J2J/9biYWQoic7Mq5U9R8pym29o4A2NjZZ1gn5PYNtq1eQnxcDIkJ8VSo/hYAJcpVYeXnE6jZoBnV6zUBoHjZiuzauJLIiDCq1W/8zK7UgAlzXjhf1ONHhN27S8kKVVGpVGg0WkKCrlOwaIl/3cbOwYn8bgW5cfkCBTwKcz84iBLlqwCwf8dGzhw9CKROyvvg3h2iHj+idKXq2Dk4AVCrQXMehNwB4MbliwyasgCAuk1bsTVgwQtnFyI7k2JK5Bn3d8zEo9tMdAWK8+TcHuJvnwOgQGtfEkIuE3vtOEH+n1LUOxD7ik2xLFiW2GvHCf5iJAVa+2JdrFq6/b1MZyr6958wJsRwc2HqvBrGpDiifztA/v8opgDsyjcm+tJPWDh7YlPmbVQqFXFB54i/dYbCvf1Qm+u4s2YwxhR9Zt4aIYQQWSDw8/H4TF2Ep1cpjuzdyR8XTgPw0dAJ3LxykQsnjjCpX1em+G2hTuOWFCtTkQsnDjN/7AA+GjqBslVqpdvfy3SmTv38A3Gx0fh2T53cNyE+lhMH99Dpk38vpgBqN3yXU4f24eZZlGpvNUalUnHl/GkunT3JhCUbsNBZMmvYxyTr5Twk8iYppkSuYF2sKiFbxuFUtwsaK3sM8dEZulNGfQJam3yYDClEX9yP9unoefrH97AsWBbLgmWJu36S5KhwDIlxmDm641S7EylRYSSF3cxQTL1MZyrm9x8p1GMOloVS577QR4YSvG4Y+Zt8itrcCqM+/pnb2ZZ5m0eHN5Bk70r+pt6pryMpFrXOFrW5jqSIOySGXAbAsmAZwvYsxhAfhdrCmphLh7AokHoDsWWhckT/fgD7Ss2JurgfS8+KL5xdCCHE85WpUpPFE4fybqcPsbF3IDY6KkN3KjE+HgcnZ1JSkjl+YHfa6HlhocF4lamIV5mKXDx9lEcRD4iPi8XFrSDNOnTnUfh9gm9dy1BMvUxn6sTBPfjO8qN4uUoARNwPYfaIPnT6ZDA6SysS4+OeuV21eo3ZtTGQfDf+oEufIQAkxMVibWOLhc6S0LtB3Lx8EQCvMhXYuGw2sVFP0Flbc/rnfRTyKgVA8XKVOPnTXt5q2prjP35PyQpVXzi7ENmZFFMiV7BwKYrz2z24s2YwKpUaC7cSuLdPPxSrc8NPuBPojcbaAZ1H2bQCJnzfcpIfhWACrItWxaJAcR4f3UjUhX2oNFq0Nk7kq9/jlbPpI++T/CQMXcG/rkU3d3RHrbMmIeQy9lXeJezbeWkDUPydxtIW8/yF0UfcxrJgWQCsi9fiyeld3FryAebOnuieLtfaOuPcsBe3V/ZLHYCiwF/fNrq2GML9b2bx+NjmtAEohBBCvD4FixSnTffezBzWC7VaQ+Hipfl01PR063R/+V7SAAAgAElEQVToNYApA7tja++EV5kKaQXMVv/5hN27g8lkomyVWnh6lWL3ltUc2/8dWq0We6d8tP7fp6+cLeLBPR6G3cer7F9fpOV3K4iVtQ03r1ykXvO2rF04LW0Air+ztrXD3bMYoXdv4lU69VLzCjXe4uC3XzK6V1vcChZJ269Dvvy069mPqYN6pA5AUbxU2n56DBrDys8n8P3WtWkDUAiRG6jc3d1NSocQonTp0gRu3MbQQ0lKRxHPsb61HW/VqkFkZKTSUYQQ4pUEBwdzKjRZ6RjiNTPXQGGLeCpWKK90FJGHyDxTQgghhBBCCPEKpJgS2UJSUhIWZvLjmN2pAAszLYmJiUpHEUKIV5ackoJGpXQK8bqpVSqSZCAMkcXk06vIFkJCQrC3NKdkPjOlo4j/UMPdgseRUSQkJCgdRQghXlnQ7Ts46pROIV43B3Mj169dUzqGyGPknimRbTRq1IhFy1aw5aqB+7EGjCb50cwuNCoVhe21tPdS0fODbpw9e1bpSEII8cpKlCjBjm92Eo0l8ckgZ5ucTQXYaE1ok57QpnUrQkNDlY4k8hAppkS28tZbb9Gtx0e4exRMndz2DXJ0dMTJKR+3gm5hMhrf6LHeBI1GSzGvYty/f5/YmJg3eiyj0UjI3TusCvDj4sWLb/RYQgiRFYoXL86gwT4ULVYMrVaZwY3VajXFinnx8GEET548USRDZjk7O2Nra8vt27cxKfQlqF6v58b168yfN1cKKZHlpJgSeVKFChXYuHEj7du35+bNm0rHeWXVqlVj1apVtGnThrt37yodRwghxEvw9/fnyZMnjBo1Sukor0ylUrFmzRru3LnDpEmTlI4jRJaTe6ZEnuPg4IC/vz9jxozJ0YUUwJkzZ1iyZAn+/v5YWFgoHUcIIcQL6t27N56enkycOFHpKJliMpnw8fGhadOmtG7dWuk4QmQ56UyJPEWlUrF27VqCgoKYPHmy0nFeGz8/P6Kjo3P0t5tCCJFXVK9enZUrV9K6dWuCg4OVjvNalC9fnk2bNuX4Kz6EeFnSmRJ5ysCBA7G3t2f69OnPXzkH8fX1pXbt2nTu3FnpKEIIIf5Dvnz58PPzY/jw4bmmkAL4/fff+eyzzwgICMDS0lLpOEJkGelMiTyjXr16LF68mBYtWvDgwQOl47x2JUuW5Ouvv+b999/nypUrSscRQgjxD2q1mk2bNnHu3Dlmz56tdJw3YsGCBWg0GgYPHqx0FCGyhHSmRJ5QoEABFi9ezODBg3NlIQVw7do1Jk2aRGBgILa2tkrHEUII8Q++vr6oVCrmzJmjdJQ3ZsyYMZQpU4YPP/xQ6ShCZAkppkSup9Vq8fPzY+3atRw9elTpOG/Ujh07OHz4MPPnz1c6ihBCiL9p3LgxnTt3pn///hhz4HQcLyoxMZE+ffowfPhwKleurHQcId44KaZErjdu3Diio6NZsmSJ0lGyxOTJk3Fzc8Pb21vpKEIIIYCCBQsyb948+vXrx6NHj5SO88YFBQUxatQoVqxYgaOjo9JxhHijpJgSuVrLli1599138fHxUWwywaym1+vx9vamb9++1KpVS+k4QgiRp1lYWBAQEMCyZcv49ddflY6TZfbu3ct3333H4sWLUalUSscR4o2RYkrkWl5eXsyaNQtvb+8cO7P8q7p37x5Dhw5l2bJl5M+fX+k4QgiRZ02ZMoXg4GACAwOVjpLlZs2ahZWVFT4+PkpHEeKNkWJK5EqWlpYEBAQwe/ZsLl68qHQcRRw6dIjNmzezfPlyNBqN0nGEECLP6dSpE3Xr1mX48OFKR1GEwWCgX79+fPDBB7z99ttKxxHijZCh0UWutHjxYoxGI0OGDFE6iqLUajUbNmzg999/Z9asWUrHEUKIPKN06dJ8+eWXdO7cmatXryodR1F16tRh+fLltGzZktDQUKXjCPFaSWdK5Do9evSgTJkyjB49WukoijMajQwcOJD27dvTtGlTpeMIIUSeYGtrS2BgIJMnT87zhRTA8ePHCQwMxN/fHzMzM6XjCPFaSWdK5CoVK1Zkw4YNtG/fnlu3bikdJ9uoWrUqa9asoU2bNty5c0fpOEIIkasFBATw6NEjxowZo3SUbGX16tWEhIQwceJEpaMI8dpIZ0rkGo6OjgQEBDB69GgppP7h7NmzLFy4kICAAHQ6ndJxhBAi1+rTpw8eHh5MmjRJ6SjZzpAhQ2jcuDFt2rRROooQr410pkSuoFKpWLduHTdu3GDq1KlKx8m2li1bRnx8PCNGjFA6ihBC5Do1atQgMDCQVq1aERISonScbKlcuXJs3ryZDh06cOPGDaXjCJFp0pkSucLgwYOxsbFh5syZSkfJ1kaMGEGNGjXo0qWL0lGEECJXcXZ2xs/Pj2HDhkkh9R8uXbrEzJkzCQwMxMrKSuk4QmSadKZEjle/fn0WLlxIixYtCAsLUzpOtleiRAm2bdtGt27duHTpktJxhBAix9NoNGzevJnTp08zZ84cpePkCPPmzcPCwoKBAwcqHUWITJHOlMjR3NzcWLRoEYMGDZJC6gVdv36dCRMm4O/vj52dndJxhBAixxsxYgQGg4F58+YpHSXHGDduHCVLlqRnz55KRxEiU6SYEjmWmZkZK1asYPXq1fzyyy9Kx8lRdu7cyaFDh1iwYIHSUYQQIkdr0qQJHTp0YMCAARiNRqXj5BiJiYn06dOHYcOGUaVKFaXjCPHKpJgSOdb48eOJjIxk2bJlSkfJkaZMmYKLiwt9+/ZVOooQQuRInp6ezJ07l379+vH48WOl4+Q4t2/fZsSIEaxYsQJHR0el4wjxSqSYEjlS69atadq0KT4+PphMctvfq0hOTsbb2xtvb29q166tdBwhhMhRLCwsCAgIYMmSJZw5c0bpODnWvn372LlzJ0uXLkWtlo+lIueRn1qR43h5eTFjxgz69OlDVFSU0nFytNDQUHx8fFi6dCkuLi5KxxFCiBxj2rRpBAUFsWrVKqWj5HizZ8/G3NycIUOGKB1FiJcmxZTIUaysrAgMDGTWrFn8/vvvSsfJFQ4fPszGjRtZvnw5Go1G6ThCCJHtdenShZo1a+Lr66t0lFzBYDDQv39//ve//9GgQQOl4wjxUmRodJGjLFmyhOTkZIYNG6Z0lFxFpVKxYcMGrly5wowZM5SOI4QQ2VbZsmXZsmULnTp14tq1a0rHyVVq1arFihUraNmyJaGhoUrHEeKFSGdK5Bg9e/akdOnSjB07VukouY7JZGLQoEG0adOG5s2bKx1HCCGyJVtbWwICApg4caIUUm/AyZMn8ff3x9/fH3Nzc6XjCPFCpDMlcoTKlSuzbt062rZty+3bt5WOk2vJ+yyEEP9u5cqVhIWFMW7cOKWj5GorV67kwYMHjB8/XukoQjyXdKZEtufo6MiKFSsYNWqUfMB/w86fP8/8+fMJCAhAp9MpHUcIIbINb29vXF1dmTJlitJRcr1hw4bRoEED2rVrp3QUIZ5LOlMiW1OpVKxfv56rV68yffp0pePkGUuXLiUpKYnhw4crHUUIIRT35708rVq14t69e0rHyRP+vDetY8eOXL9+Xek4Qvwr6UyJbM3HxwcrKytmzZqldJQ8ZeTIkVStWpWuXbsqHUUIIRSVP39+li1bxtChQ6WQykKXL19m+vTpBAYGYm1trXQcIf6VdKZEtvX2228zf/58WrRoQXh4uNJx8pzixYuzfft2unXrxqVLl5SOI4QQWU6j0bBlyxZOnDjBvHnzlI6TJ82ZMwdra2v69++vdBQhnkk6UyJbcnd3Z9GiRQwaNEgKKYXcuHGD8ePHExAQgL29vdJxhBAiy40cORK9Xs+CBQuUjpJnTZgwgWLFivHxxx8rHUWIZ5JiSmQ7ZmZm+Pv7ExgYyPHjx5WOk6ft2rWLH3/8kYULF6JSqZSOI4QQWaZp06a0b9+egQMHYjQalY6TZyUmJtKnTx98fHyoVq2a0nGEyECKKZHtTJgwgYiICJYvX650FAFMnz4dJycnucRCCJFnFC5cmLlz59K3b18iIyOVjpPn3b17F19fX/z8/HByclI6jhDpSDElspU2bdrQqFEjhgwZonQU8VRycjJ9+/blk08+oW7dukrHEUKIN0qn0xEYGMiCBQs4e/as0nHEU/v372f79u0sW7YMtVo+vorsQ34aRbZRokQJpk+fjre3N9HR0UrHEX9z//59fHx8WLJkCa6urkrHEUKIN2b69Olcv36dtWvXKh1F/MOcOXPQaDQMGzZM6ShCpJFiSmQLVlZWBAQEMGPGDBk5Lps6cuQIGzZswM/PD61Wq3QcIYR47d5//32qVavGiBEjlI4insFgMNC/f3+6du1Kw4YNlY4jBCBDo4tsYtmyZSQkJODr66t0FPEfVCoV69at4/r160ybNk3pOEII8dqUK1eOzZs306FDB27cuKF0HPEfatasSUBAAC1btpS5v4TipDMlFPfRRx9RvHhxxo8fr3QU8RwmkwkfHx9atmzJe++9p3QcIYR4Lezs7PD392f8+PFSSOUAp06dws/Pj4CAAMzNzZWOI/I46UwJRVWtWpU1a9bQpk0b7ty5o3Qc8YIqVarE+vXradeuHUFBQUrHEUKITFm1ahWhoaFMmDBB6SjiJQQEBPDw4UPGjh2rdBSRh0lnSijG0dERPz8/RowYIYVUDnPhwgXmzZtHQEAAOp1O6ThCCPHK+vXrh4uLC1OnTlU6inhJw4cPp379+nTo0EHpKCIPk86UUIRarWbDhg1cunSJmTNnKh1HvKLFixdjNBplKHshRI5Uu3Zt/Pz8aNmyJaGhoUrHEa+gTJkybN26lc6dO3P16lWl44g8SDpTQhFDhgzB3Nyc2bNnKx1FZMKoUaOoUKEC3bt3VzqKEEK8FBcXF5YuXYqPj48UUjnYlStXmDp1KoGBgdjY2CgdR+RB0pkSWa5BgwbMnTuX9957j4iICKXjiEzy8vJix44ddO/end9++03pOEII8VwajYatW7dy7NgxFixYoHQc8RrMnj0bBwcHvL29lY4i8hjpTIks5e7uzoIFCxgwYIAUUrnEzZs3GTNmDP7+/jg4OCgdRwghnmv06NEkJiaycOFCpaOI12TixIl4enrSu3dvpaOIPEaKKZFlzM3N8ff3x9/fn5MnTyodR7xGu3fvZt++fSxatAiVSqV0HCGE+FfNmzenTZs2DBo0CJNJLs7JLZKSkujTpw8DBw6kevXqSscReYgUUyLLTJw4kbCwMFasWKF0FPEGTJ8+HXt7ewYOHKh0FCGEeKYiRYrw+eef4+3tTWRkpNJxxGsWHBzM8OHD8fPzI1++fErHEXmEFFMiS7Rr144GDRowdOhQpaOINyQlJYW+ffvSq1cv6tWrp3QcIYRIR6fTERgYyPz58zl//rzSccQbcuDAAb7++muWLVuGWi0fc8WbJwNQiDeuZMmSfP3113Tt2pXLly8rHUe8YfXq1WPx4sW0aNGCBw8eKB1HCCEAmD9/PmZmZgwaNEjpKOINU6vVbN68mTNnzvD5558rHUfkclKyizfK2tqawMBApk+fLoVUHnH06FHWrl2Ln58fWq1W6ThCCEG3bt2oXLkyI0eOVDqKyAJGo5H+/fvTuXNnGjdurHQckctJZ0q8UX5+fsTExMgJLI9RqVSsXbuWW7duMWXKFKXjCCHysPLly7Np0ybat2/PzZs3lY4jslD16tVZuXIlrVu3Jjg4WOk4IpeSzpR4Yz7++GOKFi3KhAkTlI4ispjJZMLHx4fmzZvTqlUrpeMIIfIoe3t7/P39GTdunBRSedCvv/7K0qVLCQgIwMLCQuk4IpeSzpR4I6pVq8bq1atp3bo1d+/eVTqOUEiFChXYuHGjfCMshMhyKpWK1atXc/fuXSZNmqR0HKGgFStWEBUVxahRo5SOInIh6UyJ187JyQk/Pz98fX2lkMrjfvvtN2bPnk1AQACWlpZKxxFC5CH9+/fHycmJ6dOnKx1FKMzX15c6derQqVMnpaOIXEg6U+K1UqvVbNy4kQsXLvDZZ58pHUdkEwsXLkStVjN48GClowgh8oC6deuydOlSWrZsyf3795WOI7KBUqVK8dVXX9GlSxf++OMPpeOIXEQ6U+K1GjZsGBqNhjlz5igdRWQjo0ePpkyZMvTo0UPpKEKIXM7V1ZUlS5bg4+MjhZRIc/XqVSZPnkxgYCC2trZKxxG5iBRT4rVp2LAhXbt2pX///hgMBqXjiGwkMTERb29vfH19qVixotJxhBC5lFarxc/Pjw0bNnDkyBGl44hsZvv27Rw9epR58+YpHUXkIlJMidfCw8ODBQsW0L9/fx4+fKh0HJEN3bp1i9GjRxMQEICjo6PScYQQudCYMWOIjY1l0aJFSkcR2dSkSZPw8PCgT58+SkcRuYQUUyLTzM3NCQgIwM/Pj1OnTikdR2Rje/bs4fvvv2fRokWoVCql4wghcpH33nuPli1b4uPjg8kkt4OLZ9Pr9Xh7e9O/f39q1KihdByRC0gxJTJtypQp3Lt3D39/f6WjiBxg5syZ2NjYyGAUQojXplixYsyePRtvb28iIyOVjiOyuZCQEIYNG4afnx/Ozs5KxxE5nBRTIlM6dOhAvXr1GDZsmNJRRA6RkpJCv379+PDDD6lfv77ScYQQOZxOp8Pf3585c+Zw4cIFpeOIHOLgwYNs3bqV5cuXo9FolI4jcjAZGl28MhlmVGTGn0MXt2rVitDQUKXjCCFyKJl6Qbwqmc5FvA7SmRKvxMbGhsDAQKZOnSqFlHglv/zyC6tWrWLFihWYmZkpHUcIkQN1796dChUqMGrUKKWjiBzIaDQyYMAAOnToQNOmTZWOI3Io6UyJV+Lv78+TJ0/kBCYyRaVSsXr1au7evcukSZOUjiOEyEEqVKjAxo0bad++PTdv3lQ6jsjBqlWrxurVq2ndujV3795VOo7IYaQzJV6IRqNJG32td+/eeHp6MnHiRIVTiZzOZDIxZMgQmjRpQps2bYDUnzW1Wv40CSEy+rOL7eDggL+/P2PGjJFCSmTamTNnWLRoEQEBAVhYWADIFRPihUlnSryQkSNHEhYWxqVLl1i5ciWtW7cmODhY6VgilyhXrhybN2+mffv2dOzYkbCwMNatW6d0LCFENmJtbc3BgwepXbs2a9euJSgoiMmTJysdS+Qifn5+xMTEMH78eI4fP0716tVlmH3xXPL1r3gh1apVIzIyEj8/P4YPHy6FlHitLl26xKxZswgMDOTBgwdUq1ZN6UhCiGymXLlyhIeHM3DgQOzt7Zk+fbrSkUQu4+vrS82aNWnXrh0GgwFPT0+lI4kcQIop8ULKly9Pz5492bFjBx4eHrRu3VrpSCIX+eCDD9Dr9Vy4cIEmTZpQvnx5pSMJIbKZChUq8PDhQ3r16sXcuXOZMGEClpaWSscSuYSLiwvDhg1j8uTJjB8/ntu3b1OhQgWlY4kcQIop8VyFChXCwsICFxcXOnbsSKNGjWQEP/FaXbp0ia5du1KrVi3Kli2Ll5eXfEgSQqRTs2ZN6tWrx507d1i8eDGhoaEkJCQoHUvkEpGRkSQkJLBkyRJu3rxJpUqV5CoJ8UKkmBLP1axZM3Q6HTdu3ODDDz/ko48+4vr160rHErnIuXPn6Ny5M0OGDOHOnTtotVrq1q2rdCwhRDZSv359UlJS2LVrF3Xr1sXf31/pSCIXSU5OZu7cudStW5eDBw+i1Wpp0aKF0rFEDiADUIjncnBwoGjRopw7d07pKCKPqF27NufOnSMpKUnpKEKIbKJOnTqcPXtW/i6ILGFra0uJEiU4e/as0lFENifFlBBCCCGEEEK8Aq3SAbKSq6srQ4aPoFjxkpiZmysdR/xNdNQTTh47wgq/5TIMaQ7XuXNn2rRtR/78zkpHyVWePIniyOHDLF++TH5HciBv7740aNgQBwcHpaOIF2A0mbgXEszKwEBOnTqldBzxD9WrV+fj3n3wKFhI5iXMZh48uM/XWzfzww8/KB0ly+SZzpSrqyvbvvmWczE2nA83kGRQOpH4OzsLFW2LqTl/eC+jRwyXD4s5VL9+/RkydCjJGh1G+X/4WqkBdUoie7//juHD5XckJ5k16zNatetAjMkcg1HpNOJFWWhV2Kv1fPJxL44ePap0HPFUrVq1WLNuAxcj1UQmGJG/hNmHCrAxV1PV2cSEcaPZvm2b0pGyRJ4ppgLXrCeqQDU2/J6odBTxLyy1Kma/Y8mssUPZu3ev0nHESypZsiS7d39PjMkcY574q5L1VICVKZGhQwbL70gO0bhxYxYtXU5okvxe5ERWZioKmCdTqlRJ+QIjm7h05Q8O31dzL0a+Fc+uHHVq2nhpqVmjOpGRkUrHeePyTG+0ZKlSHAlJUTqG+A8JKSbOhpsoUaKE0lHEKyhZsiTxSXr5wPgGmQCjxkx+R3KQEiVKkGjUyO9FDhWfbAKVChcXF6WjCMDJyQmtmZkUUtlcZKKRhzEJeHl5KR0lS+SZe6YszM1JTHmzZzNjShKhawZjMiRjMhqwKfMOTg0/BuDemoEYk1LnwzDERWLhUQa3rjPeaJ5/Sgq9SvjOWRiT9ViXqEW+dwejUqkyrJdw+xwP9y7FZExBY2WPx0eLSYkKJ+ybGRhiI0Glwq5qaxxqd0rd74MbROyeh0mfgNahAK4dJqC2sH61jCY1Op3ML5QTWVhYgOrVvp+JiYlmzrSJBN24jkqlYtSk6ZSrWDnt+a0b1uC3cA7f/HgMB0dHtqxfxf493wFgMBi4G3SLb348ip191t2PotfrmTVxNFevXMLe3oGJn83Hzd3jmesaDAa8e3TGOb8rny3yA+DzqeO5evkSJpOJgoWLMHryDKysrNmzawcrFs3F+emHt/ZdutOqfae0fWk0WpmDKwfR6XSoNFrIoouRHoY9YMmMsUQ9fgQqFU3bdKJl5w8AmD/Jl9C7twGIi43B2saWuWu+zpJcfwoLDWHB5JHERj+hWKmyDBo/CzMzs3TrhN+/x5AP2uLuWQSAEuUq4u07EYDpw/sS+SgCg8FAmUpV6T10HBqN5o2+tuQUAzqd7rXsS2SOTqdDn/zmCqmYRw/4wW8y8VGPQQUVGrWnyrvdAEiMjeL7JWOJjriPXX43Wgyehc7ajl+/28Afx/YAYDIaeHzvNt4r9qGzsX9jOaMj7vPtwhGYjEaMhhQqN3ufik06ZljvyKZF3Dp7BI3WDHvXgjTtMxGdtS2GlGQOrJpJ2K0rqNRq3ukxnEJlU+fUOvblcq4c2U1SXAwDVh9+5Yx6gynP/N7kmWIqK6g05rj3XIDa3AqTIYV7awZiVaIWuoLl8Oi1NG29B19OwLrUWy+8X0NCNBpLu0zni9g9n/ytR2DhUZb7m0YSf+Mk1iVqpz9WYgwRuxfg9sEczOxdSYl72p5Va3BuNgALt5IYk+IJCfgUK6/qmOcvQsS3n5OvaX8si1Qm+txunhzbglOjTzKdV+QdS+fMomadekz9fCHJyXoSE/+6HDf8wX1+PfELrgXc0pZ1/fATun6Y+jP2y+Gf+Grj+hcupIxGI/FxcdjY2mYq8/ffbMPGzo5NO3/gwA/fE7B4HpM+m//Mdbdt3kDhIl7ExcWmLRswbDTWNjYALJs/mx1bN9G916cANGz2HkNGjc9UPpE3aTQaeg7wpVipsiTExzHyk/epWL0OhYp6MWzK3LT11i2dg5W1zQvvNzEhHo3WLEPh87K+WLGAVl16UK/Je/jPncrB77bTvP37GdZz9Sj0zGJo2NS5WFnbYDKZmDthGMd/2ke9Ju9l6rUJ8Se1Wsvb3YfgUrQ0+oQ4No3/EM/ytchXsBind62jULka1GjzEad3reX0rnXU7zaI6q16UL1VDwBunT3M2T2bX7iQMhmN6BPjsbB6uZ9Xa0dn3p+8Gq2ZOfrEeDaM6kqxam9j45g/3Xqe5Wvx1vsDUGu0HNm8hNO71lK/2yB+P7gDgB6ztxAf9ZhvPveh27R1qNRqilWpT+WmXVg7vMNLZcrL8sxlfpn1+Od13F36AfdWDyRs2xSe/LIlwzoqlQq1uRUAJmMKJkMKqXc5/MWYFEdC0FmsS9f/z+OZTKb/s3eW8VFcXwN+VmK72biQkJAQIpBAgru7S6FAKVAo7lJcU9ytuLtTpMUdggUrTnAn7tlssvZ+2LKQd0MgQNt/yzxfkpnr88vJ3HPPmXNQPr5C1I5xvFjW7bPnr0mJQ5ehxNIjCJFIhCK4Dsq7ph/Upt44grxQZcxsXQGQyu0NPxWOWLj5AyC2kGHm7IUmOQYAddwLLL1CAJD5lCL1zsnPnq/Af4O1yxbR7pv69P6xLeNGDGLz2pUmdVJTUrh29RINmhpO1czMzFEo3h4ezJ81lW79foJsrKgARw/so0adDydWjHz9klVL5tPum/rc+OPz84acOXmMug2bAlClRm0uh5/P9puK6KhIzoedNK7vDW8UKb1eT4ZKla2VWEDgXbatXkzfNo0Y1bM9s0OHsHvTapM69k7O+AQEAmAlk5PXOz/xsVFZ6uj1es4eP0jFmh+Wm/u3b7Bk+s8MaN+MtJSkz5q/Xq/n5pVwylWtBUDVuo0JP30sV328UZK0Wg0atdpEbnKzNoGviws7l7NmUHO2/tyZffNHcnnvOpM6cnsnXPIXBMDcSo6DuzepCYa9zqMrJwms1BCAwEoNeXT5hEn7iLOHCChX+4NzSY55zbkdS1kzqAWvIv7I9VokUjOkZoao1Fp1Juizj2zjFVwWscRgN3HzLUxqvOF/QdzLx3gGlgJAZuuAhdyaqMd3DPX8iiC3F6Lx5gbBMvURqF7eIe3OKTy6rwCtlhdLO2PhFpBtXb1Oy4ulXVHHv8S2VFMsPQKzlKfdPY1V/hLvdYPTpMSS8sd+Uq4fwtzZG0XR+rg0G2ksf9dd8F0ca/dA5lPyvWvQpMQgtXl7YiG1cUaTEmtSTx33wmBVW90PXaYSuzLNUYTUzVon8TWZr+8b12bm7B6XBtIAACAASURBVI0yIgx5wUqk3j6OJjn6vfMQ+Hq4e+sGJ48dYvmmnWg1Grp83xz/goEm9V6/eoGdvQNTQkfy8P5d/AsG0WfwcKysZISdOIqzswu+/gWzHUOVnk74udP0Gzoy23K1OpMzJ46xd9cOEhLiqNOwKQtWbcLO3nBI8K674LuEFCtJ3yHZ9/mGmJgonF3zACCVSrG2VpCUmGjs+w3zZ06hW79BKNPSTPqYEjqCC2dO45W/AD0HDDHeP3X0ENevXMLDy5veA4fi8o5VTuDr5MGdm1w4eYQZq7aj1WoY3KmlUWl6H9GvX/Lk3l38AoOz3L9z7TK29o64eXpl2y4lOYlTh37n+L5d2No5UL1BM37sN9yYUuRdl7p3adiqPVXrNn7vfFKSEpFbK5BIDVsPR+c8xMdm/76Ifv2SQT9+i0wmp3WXPgSGlDCWjR/YjQd3blCsbEXK/qmYfezaBL5OIh/e4n74Mb6ftBGdVsPGke1wzZ/9e+UNSTGviHkaQZ4CQQCkJcUblQyZnSNpSfFZ6qszVDy5fo5qHQZn259Wo+bh5ZPcPL6b9OR4ClVqQKufV2KlMHhVvOsu+C4eBYtT9YdBJvdT4iLZPX0AiVHPqfRdXxOr1P/n1sk9+Jc1yIuzlx+PrpwioHxtUuKiiHp8l5S4KONaBXKHoEx9BKrnN5EHVEAstQApyPzLv7euSCzBs/sKtKoUIreMIiP6ERYuPsbylJtHsSnWMPtxXt7h5Ype2BRvQN6OvyCRmbotvesu+Feg12nJeH0P9/az0GsyeLmiJxYeQZg7egKgy1QSuXUMjnX7GBVClyZDid0/j/hTa5H7V0Ak+Tw3EIH/BjeuXaVileqGb6ksLChfuVq29bRaLffu3qbv4BEEFgnhl+mT2LhqOd937MKGlUuZvmD5e8c4e/oEhUOKv9fFr1vblmi1GoaOnUhgkRCT8nfdBf8Kzp46gb29AwGFgrh6yTRXzbDQSWi1WuZNm8jxw/up1/gbyleuRo26DTA3N2fPji1MHjuC2UtW/WVzFPh3cPfGVUpVrIa5hQVgQcnyVXKsn65UMmPUADr0HWri8hZ2ZP97LTfxsdH0alWPYmUqMmzyLzj9eWDwLu+61P0V2Ds6s3j7IRS2djyMuMW0Ef2YvXaXcR2jZy0hMyODueOHcfPKBUJKvX0n57Q2ga+XV/euUaBEFaTmBvnJXzxn76BMlZK9c4ZSpd3AbF3wRCIRov/nefToyinc/YPf6+K3aVR7dFottbqNwc23sEn5u+6CH4PCMQ9tp2wiNSGG32YNwrdMDeS2jtnWDd+1ErFESsEK9QAIqtKY+JdP2DiqPTZObrj7BSMS8nV9MoIy9RlokqJ5vWk4ADYlG2NbsomxTGKpwMq7GOkPwo3KlFaZSMbLu8haTci2P3NXH5wbDyHl6l4iN49EEVIX68LVs1ixPtYy9cZCBiAPKI9NyaZGtzwATXIMUoWpGVdq44zEygaxuRWYW2GZL4TMyAeYO3qi12qI3DoGRZGaWBeq/HbeTl64t5sJQGbcc5T3z3344Ql8tURHvmb4gJ4ANG7eiopVa+Ds4mpUdqrUrM3GVct59eI5r1+9pNN3zQCIiY6i6/fNWbR2C45OhhO4YwdzdvEbNOpnft+5jYmjh1KpWk3qNW6GV/630YVyY5lavmAO58IMLqwrNu3E2dmVmKhIXFzzoNFoSE1Nwfb/JWS9ee0KZ04d5/yZU2RmZqBMTWPCqCGMmjDNWEcikVC9Tn02rVlBvcbfZOmjQdMWLJk788MPVeCrJTYqkinDegNQq0lL6jRtiUajZsaoAVSq1YCyVWpmqa/VaLhw6gjTlm/Jtj9be0f6jZnKsd9/ZcrwPlSp04jKtRtga/92k5Yby9T4gd1ISoijQEAQ3YeGkpaaglajQSKVEhcTiYOTaZQ8M3NzoxWsQEAQru6evHr+FN+Cb0/NzS0sKFWxGhfDjhuVqQ+tTUDg/5MSF8nuGQMBCK7RnOCazdFqNPw+ZygFK9TFt1R1Y125rQNpCbHI7Z1IS4hFZpvVC+He+cMElKvz3rFqdB7JzWM7ObhwDAVKViWoSiMc8uY3lufWMvUGa3tnHD0L8OruH/iVqWFSfuvkbzy6GkbzEQuNbrFiiZQq7QYa62wJ/RH7PPneO4ZAzgjK1Edg6VmYmN9nYlfpe9BpUd47h02JRkhtXfDsvsJYT5uWCBIJEksFOnUG6Y8uYVehjbE89fZJZP7lDBaubBBLLbApWhebonXJjHtBytW9PF/SGUuPIFy/MXyM/rGWqTcWsiz9W8hQvbiFRd5AUq4fxLa0aeQXeUAFYvfPNX7zpXp5B9ty36LX64neMxVzJy/symX9WFiTloBUbo9eryPh1FpsSr7fzUPg66FISDFmTgylTceuaLUazp0+QcNm3+KSx40Vm3ZmqevimodnTx6Tzzs/l8PP4+VTAB8/f3YdeftdX6uGNVmybpvRjS41JYVrVy4ycsLU984hsEgIgUVCUCrTOH7oANPGjUan0zFg2Bj8CwXmyjLVuVd/Ovfqb7wuX6UaB37fRVBwUU4ePUTxUmVMvt/o2mcgXfsYXlhXL4WzZd0qRk2Yhl6v5+WLZ3h4eqHX6zlz8hj5vA0v1biYGBydDcri2ZPHyZffBwGBgkWKsWT6OJq17YxWq+Xy2VPUbNwCJ9c8WQI16PV6Fk4Zi4e3D41a/2DSz/XL58mbLz+OLqYWJzAo92Wr1KRslZrExURxfN8uRvfuQN58+ek9ciJya0WuLFOjZy3Jch1UrBTnThymYs16nDiwh1KVTC3WSQnxWNvYIpFIiHr1nMgXz3B19yBdqUSlTMPeyRmtRsOVc6coFFz8o9cm8PXi7h/C0ZWTKdW4AzqdlsdXwyhSvanBujN5o7GeXq/nyLLxOOT1pnj977P04VO8MrdP/06pxh24ffp3fIq/tQ5nKFN5cecKdXuMe+8c3HwL4+ZbmEyVknvnD3N42Xj0Oj3VOw7FJX/BXFmmUuKisFLYIjW3RJWWzKuIaxSv28ak3pNrZ7n8+zpajF6CmcXbyHrqDBXo9ZhZWvH0xgXEYimOHsK75lMRlKmPwDJvIeQBFXix6Eck1vaYu/pk+82TJjWO6F2TQKdDr9djHVQV+Tsugak3j2Ff0fSPPTvMHT1wrNkNh+qdUN47/0XW4dRgANG7pqDXZCDzLYPMtwwASZd2A2Bbsgnmzt5YFSjN80U/gkiMTfEGWLj4kP7sOqnXD2Hu4sPzxYbNp0ONLsj9ypJ64yjJFw2bY3mhyiiKCi4WAlAwqAjlq1SjU+um2Ds44uPrj7V19hH0+g4ZyYRRQ9Co1bjl9WBY6IfTBpw+foSSZStgZSX7YF2ZTE6Dps1p0LQ5Tx8/zPVasqN+k+ZMGj2UNk3qYGNrx5hJhg1mbEw008ePZuq8Je9tq9frmTJ2BGmpqejR4+sXwIDhYwHYsXkdZ08dRyKRorCxZVjopC8yX4F/N76FClOyYlV+6tAcWwdH8hXwyzZi3d0bVzl18Dfy+fgxqKMhpH6brn0pXs7gTXDmyH4qfKQbnKOzKy1+6Ebz9l25ecXUTfVTaNdjALNDh7B5+S94+xWkRgNDxLCLYcd5ePcWrTv35s61y2xesQCpVIpIJKbroNEobGxJjI9lyvA+qDMz0ev1FC5WitpNWhr7zs3aBL4u8hQIwqd4ZdYPb4PM1gEnzwKYW5nKz6t717gTtg8nT1/WDzfs1yq06kX+ohUo2egH9v0ynFsn9qBwykODvpON7R5cPI5XkTKYfURqF3NLGYWrNqFw1SbEv3z8SeuJf/WE0xvmGAIz6fWUaPA9Tvl8ATi8bALBNb7B1SeQ42umo1Vn8uvkXgC4+RahRqfhKJPj2TW1D4jEWNs7U6fHz8a+T2+cR8TZg6gzVSzv3YCgak0o17zrJ83za0Hk7u7+VaQSPH/pKmMviohM/bT8BLpMJWJzGTq1iler+uLcaJAxup3Al+O7wnLijq1k+vRpH64s8D9F8+bNGTNuImrpW+VGqUxDJpOjSk+nb5f2DBr5M/6Fcv5oXiBnrM1ErFz8C9OmCTLyb6B///60696fmPTPf9WmK5VYyWRkqNIZ3bsD3QeP/WAQCoHPx9Myk/p1a/P06dN/eipfPe7u7hw8eoIt93K/l8tUKTG3lKHOULFtfFdqdhphjNwn8OWplVfDyP49CAszjRz9X0OwTH0kMb/NIDPmKXpNJoqQOoIiJSDwEcycEMqTxw/IzMikbsMmgiIlIPAZLJkeyosnj8jMzKBq3SaCIiUgkAuOLp9E3MtHaNWZFKrUQFCkBL4YgjL1kbg2H/NPT0FA4F/H6EnT/+kpCAj8Z+g/VrBGCgh8KvV6Zx/8S0DgcxHiIP6NxB9fgfLRpRzrpEWcISFsw2ePlfEqgueLOvB0XhtDQIlskolmxj7lxYoePJxQM9skxHqdludLOvF647DPno+AwKeyctEvXLpwNsc6Z04eY8OqZZ89VsSdW3Rs2YQ2Teowb9rEbOXmDXdv3aB66SKcOHLQeG/JvJl0aNmYDi0bc+zQ26hMl8PP0aVNczp914zeP7blxXPBXUjg72Xz8vlcv5RzpNWLYcfZuf79qQg+locRtxj4QzN6t67PijmTc5SjB3du0rJqUc4dP2S8t27RLAa0b8aA9s04c/SA8f6ccUPp26YRA9o3Y8Hk0Wg06s+eq4DAx3Ju+2Ke3byQY52Hl09ycc/qzx4r6vEd1g1tzaqBzTixZkaOMhT58BZz25Xl/oWjgCEh8IaRbVk/vA1rh7Tk+pEdxrpajZojyyey+qfmrBnUgvvhuUuaLZA9gmXqb8Sh2oejhskDKiAPqPDZY8XsnYVzo8FY5A3k9cYhKB9cQO5XNksdsZUNTnX7knY3e3/WpAvbMXfyQpeh/Oz5CAh8Kj/26PPBOhWqVKdCleofrPchZk8ex6DR4wgsHMzQvt0IP3uaMhUqm9TTarUsmTeLUmXfBpg5d/ok9+7eZvnGX1GrM+nftQNlyldCbm3N7MnjmDhrPl75C7Br6ybWLV/C8J+FwBICfx+tO/f+YJ1SFatRqmL2+eByw7KZE+g+JBS/wGAmDu7B1QthFC9rmtdHq9WyfvFsQkqVM967fPYUj+/dYcbKbajVmYzt+yPFylZEJremcq0G9Bs9BYA5Pw/l6G+/UqdZK5N+BQT+Csq16P7BOgVKVKFAiZxzwH0Mx1ZOoWbnkeTxLcyuaf14cu0s+Yua7g11Oi1hm+fjVaSM8Z7c3olWoSuRmpmTqVKybmhrfEpUxtremfBdK5HZONBh5g70Oh2qtOTPnquAoEz9JcSfXEPqjcNIZHZIbZ2xcAvArnxrondNRuZfDuvAqjyd0wpFSB3S7p0FnQbXb3/G3MmL5D/2k/EqAuf6/T880HvQpMShy1Bi6WHIyaEIroPybpiJMiWV2yOV26O8bxotUJMcjfL+eewqtSPp3NZPnouAwMeydtkiDu//DVs7B1zy5MG/YCCt2//I5LEjKFepClVr1qFVw5rUbdiEs6dOoNFoCJ06G6/8Puzfs5OIO7foP3TUJ48fFxNDWmoqQX/mu6rToAlhJ45mq0z9umUDlWvU4u6tm8Z7Tx8/IKRYSaRSKVKplAJ+/oSfPU212vUQiUSkpaYCkJaagpNzzpnqBQQ+lW2rF3P60F5s7OxxdMmDT0AgTb7rwPyJIylRvgrlqtWmx7d1qFq3MZfOnESrVfPTuJnk9fLh+L5dPIy4RecBIz880HtIiI1BmZaKf5BBjqrWbczF08eyVab279hImSo1eXjnrRy9ePKQQiElkEilSKRSvAr488eFMMpXr2uMSAiG6IZxMVGfPE8BgfdxYedy7p7Zj5XCHmtHV1zzF6REg3YcXByKT7FK+JWpwYp+jQms1IBHV0+j02ho0G8KDu7e3Dr5G9GP71Ctw5BPHj8tIZbM9DTc/IoAUKhSAx5ePpmtMvXHwS34lapG5KPbxnsSqZnxd606E/Q64/Wtk3v4YbohlYJILMZKkX3Ce4HcIShTXxjVyzuk3TmFR/cVoNXyYmlnLNwCsq0rkdni2W05SRd3knh2Cy6N3y986Y+vEHtwgcl9kZkFHp0WZrmnSYlBavN2sya1cUaTEpurdcQemI9jze7oMgWrlMBfz91bNzh57BDLN+1Eq9HQ5fvm+BfM/uN6Wzt7lm3cwa6tm9iybhVDxox/b79XL15g/qwpJvctLa1YsGpjlnsxMVE4u7oar51dXYmJjjZpGxMdRdjxI8xespq7t94qbwX8CrJm2UJatu2ASqXi6qVwY4LgwaPHMaxfd8wtLJHL5SxcbepWKyDwuTy4c5MLJ48wY9V2tFoNgzu1fG+QCoWtHdNXbuXAzs3s2bSGHsN+zrYewM0r4az+xfR7LXNLSyYtWp/lXlxsNI7Ob+XIwdmVuBhTOYqLiSL81FFC561k4TvKlJdvANtWLaJx6x/IUKm4eSUcD+8CWdpqNGpOHfydjv2GvnfOAgKfQuTDW9wPP8b3kzai02rYOLIdru8JVGGlsOP7ieu5dngbl/eup1aX9x/mPb91iZPrZ5ncN7OwpFXoyiz3UhOisXZ4m8xa4eBCWnyMSdvU+GgeXjpBi5GLiVyaNb9VSlwku6cPIDHqOZW+64u1vTOqtBQAzm5fzIs7l7Fz8aBqh8HIbR1N+hbIHYIy9YVRPb+JPKCCITGvFGTv5Jn6/8gLGU7ZLNwCSLtzOsd+rfIXN0nC+1eRdu8sErkdFu4BpD+5+reMKfB1c+PaVSpWqY6FhQVYWFC+8vtdjSpVrwWAf6FATh0/nGO/xUqVMUkQ/LnMnzGZrn1/QizO+slpqXIVuHv7Br1+bIOdvQNBRUIQSyQAbNuwlilzFxNYJITNa1ewYNbUHJVAAYFP4e6Nq5SqWA1zCwvAgpLl3+9uVKZKTQB8/AO5cPJIjv0WLl46S2LgL8GqeVNp22OAiRwVLV2eh3dvMrJHO2zs7PEvHGJSZ9nMiQQWLUFgSIkvOicBgVf3rlGgRBWk5gYZyl/c1KL6Bt9ShveUS/5CPLh4PMd+PYNKZkkO/CU4uW4WFVv3QSQ2DX+gcMxD2ymbSE2I4bdZg/AtUwOxWEJqfDTufsFUaTuAK/s2cHrDXOr2fH+iYYGPQ1Cm/kFEb0yxYjF6Xc45E3JjmZIqnNEkvz3F0CTHIFU4ffS8VM9ukhZxFuX9C+g1megy0oj6dQKu33y6C5WAwJfCzMwcALFEglabs9zkxjLl7OxKTNRbt6GYqCicXVz+f1Mi7txi3PCfAEhKTODCmVNIJBIqVatJu07dadfJ4Fc/fsRgPPN5kZgQz8N7EQT+6T5YrVY9hvQREiAK/LPkRo5yY5lydHLJ4n4XHxOFo7OpHD2KuM3sUIM3RkpSAlfOhyGRSChduQbN23eleXuDjMz5eQjunl7GdltXLSI5MZ5ug+d85EoFBP4aJG9kSCxG94E9XG4sU9b2LqTGv7XmpsRHI3cwdQ2PenyHffMNLrmqlESeXDuLSCLBt2TVd/pyxtGzAK/u/oFv6epILSyNSqBfmRrcPLH74xYrkCOCMvWFsfQsTMzvM7Gr9D3otCjvncOmRKPP7jc3limpwhGxhQzVi1tY5A0k5fpBbEs3/+ixHGt2xbGm4UWW/uQqiWe3CIqUwF9KkZBizJwYSpuOXdFqNZw7fYKGzb797H5zY5lydHZGbm3NrRvXCCwczMG9u/mm1fcm9Tb/9tYa9uZ7rkrVaqLVaklNScHWzo6H9yN4+CCC4WUnA5CamsLzp0/w9PLm0oVzRvc/AYEvScEixVgyfRzN2nZGq9Vy+ewpajZu8dn95sYyZe/kjExuzb1b1/ALDObEgT3Ub97GpN7CrW+j9L35nqt05RpotVqUqSkobO148iCCpw/vE1LK4OFx5Lcd/BF+hrFzlptYqwQEvgTu/iEcXTmZUo07oNNpeXw1jCLVm352v7mxTMntnTC3kvP6/g3y+Bbmzum9FK1jGmjlxzlvFaE333P5lqxKSlwUVgpbpOaWqNKSeRVxjeJ12yASifApVokXdy7jGVSKZzcv4pjX57PXJiAoU18cy7yFkAdU4MWiH5FY22Pu6oPYQv63z8OpwQCid01Br8lA5lsGma8h0kvSJYPw2ZZsgiY1jhdLu6HLSEMkEpN4fjv5eq35R+Yr8HVTMKgI5atUo1Prptg7OOLj64+1teJvn0f/YaOZEjqCTFUGpStUMgaf2L3d8I1Tkxat39tWo9HQt3NbAGRya0aOn4pUavgXO3jUOMYM7odYLMbaxoahY4R8JwJfHt9ChSlZsSo/dWiOrYMj+Qr4IZNb/+3z6DxwFAsmjSIzQ0WxshUp9mfwiYO7DMGM6jRt+d62Wo2G0b1+AMBKbk3f0ZOR/ClHS2eOx9nVjZHdDXJWpnINvu3Y469cisBXRp4CQfgUr8z64W2Q2Trg5FkAc6u/X4aqdxzKoSU/o8nMwDukPN4hhgOFN2HOg2u+/4A8/tUTTm+YAyIR6PWUaPA9Tvl8AajYug8HF43l5LpZWNnYUavr2L9+MV8BInd39/cHr/8Pcf7SVcZeFBGZmrMp9kugy1QiNpehU6t4taovzo0GYeHm/5eP+1/gu8Jy4o6tZPp0ITnlv43mzZszZtxE1FLZJ7VXKtOQyeSo0tPp26U9g0b+jH+h7D+e/5qxNhOxcvEvTJsmyMi/gf79+9Oue39i0v+eV226UomVTEaGKp3RvTvQffDY9wahEPg4PC0zqV+3Nk+fCvnh/mnc3d05ePQEW+79dXu5TJUSc0sZ6gwV28Z3pWanEbi8JwiFwPuplVfDyP49CAvLPv3OfwnBMvUXEPPbDDJjnqLXZKIIqSMoUgICH8HMCaE8efyAzIxM6jZsIihSAgKfwJLpobx48ojMzAyq1m0iKFICArnk6PJJxL18hFadSaFKDQRFSuCDCMrUX4Br8zH/9BQEBP51jJ40/Z+egoDAv57+YwWLpYDA51Cvt+CGLZA7hC84BQQEBAQEBAQEBAQEPgHBMvU/xMvV/XCs3QNL97/PpBx3dBkp1w+iS0/FZ8Tb6EpJl3aTfHEniCSIza1wbjQIc2dv9FoN0b9NI/P1PfQ6LYrgOthXMnwMnHhuK8lX9wIiLFzz49xkmCHfloDA/wD9uv5Aj/6DKRhY+G8ZT5mWRp8/A1KAIdR6rfqN6DNoOPv37GTx3Bk4/Rl6vVnL72nY7POjrgkIfApj+nSkfa9B+BYM+tvG3Lh0HicP7iEtJZn1h8JNys+fOMyM0QOZsmwzvgWDUKvVLJ3+Mw8jbiESienYbxiFi5UC4GHErT8DXmRQrGwlfuw3DJFIxJMHESydMQ5VuhLnPHnpN2bKPxKQQ+DrY9uEblRu0w9Xn7/HzVadoWLvvGEkRb1AJBbjU7wSFVv3MZbfO3+Y8zuWgQic8/lTr/cEk3DtCa+fUq/3RHxLVuX5rYuc2jgXnUaNS/5C1OoyCrFEUBneh/BkvnLkAeWxLf0Nz37JGgJaUaQmtiWbAJAWcYbYgwtwbzud1NvHQaPGs8dqdGoVzxf8gHWRGojEUpLCd+DZcy1iMwsit40l9eYxbIrW+yeWJSDwjyOTy7OEZe/6fQsq/5lwGKBa7Xr0HyqkHBD4OilZoQr1vvmOPm0amJSlK9PYu309foHBxntHfjOEZp+1ZidJCXFMHNSDKcs2IxaLWTZzAt2HhOIXGMzEwT24eiGM4mUrsWjqWNr3/ImgYqU4uncnuzet4rvOfUzGExD4L1Cifls8g0qi1ajZMaknj/84Q/6iFUiIfMbFPatpGbocS7kNyqR4IGu4dlVqEqsGfoNXkbLodToOLg6l+YiF2Lt5cW77Ym6f3kvhqk3+yeX9TyMoUzmgy0wnansomuQY9DodDpXbY124OvEnV6O8dxadOhNLzyCcGw5CJBLxcnU/LPL4oXp2HZ06HZemI0gM20Bm9CPkQdVxrN4ZdeJrXq8fgoW7Pxmv72HunB+XZiMQm1lmGVv58CLxJ1ai16gxc3DHpckwxOYy4o4sIS3iDCKxBKsCpXCq3fOz1mjpkf1J5Lvh0XWZ6SB6cyVCp05Hr9OgV2cgkkgRW8jRqzPQ67ToNRnoJRL06oxcJQoW+PpIT1fy89CBxERHodVpad+5B9Vr12PN0oWcPX2CjAwVhYOL8dPIUEQiEf26/oBfQCGuX72MKj2dEeMms2HVMh49uEe12vXo3LMfr1+9ZEjvrgQUCuTe3Tt4F/BlxM+TsbSyyjL2xXNnWLVkPurMTNw9PBkaOhGZTM6SebM4e+oYEomUkmXL03PAkC+y1udPn5CQEE9wsRJfpD+B/zaqdCWzxgwiLiYKnU5Hix+6UaFGXbatWsSlsyfJzMggoHAI3QaPRSQSMaZPR/L7FeTO9StkqNLpM3ISv65fzrNH96lQvQ7fdelL9OuXTBjUHZ+AQB7fu4Onty99Rk3EwjKrbPwRfpatKxegzlTjmteDXsMnYCWTsX7xbC6FnUAskRBSujw/9Br0WWv0Dwp5b9nm5fNp2qYTuzetMt578eQhhYsbUnzY2jsis7bh4d1bOLnkQZmWauyvat3GXDx9jOJlK/H6+VMCi5YEIKRkOSb81E1Qpr5S1Kp09v4ynNT4aPQ6LaWbdiKgXG3O/7qMx1dPo8nMwM0vmBqdRiASidg2oRsuXgG8jPgDdUY6dbqHcnHPauKeP8S/bC3Kt+xBUswrdk3ti0v+gkQ/icAxrw91evyMmUXW/dzT6+c5t2MpWk0mdi4e1Oo2BnNLGWGbf+HR5dOIJRLyFSlD5e/7f/L6zCws8Qwy/K1LpGa4eAcYE//ePLaLkFrfYim3AUBm62DS/n74UbxDymFmYYkyOQGJ1Ax7N0Oy7HyFy3Bxz2pBmcoBQZnKAeWDcCTWjri1mQqAVpUKgG3pb3Co0gGAqJ0TUN47izygQe/JjwAAIABJREFUAgAiiRSPrktJPL+dyM0j8ei6DImVgqfz2mBX1pCEVB33DOfGQ7DKV4To3VNIvrgLu/Jv89dolYkknFqLe7tZiM2tSAjbSOK5rdiWakba3dN49lqHSCRCq0oxmXP64yvEHlxgcl9kZoFHp4W5Wn9S+E4Sz29Fr1Xj3t6Qbd46sCrKiDM8mfkNenUGTnV6IbGyASuwK9eap7NbIjIzR1agFLICpXI1nsDXRfjZMBydXZgybzEAqSmGv+dmrdrwQ1fDIcHE0UM5d/oE5SsbMrabmZmxdP02tm9cx8iBvVm6fjsKW1vaNKnDt20MuWmeP33MkDHjKVK0OFN/HsmubZto3f5H47iJCQmsW7GYmYtWYGUlY+Pq5Wxbv4amLdsQduIIa3fsRSQSkZKSbDLnqxcvMH/WFJP7lpZWLFj1/oSMxw7uo1qtuohExlMJTh09xPUrl/Dw8qb3wKG45HHL7SMU+I/yx4Uz2Du5MGK64X92WqpBNuo2b2PMqzRv/HAunz1JyQpVAZCamTFt+Rb2blvP1OF9mbZ8C9Y2tvRqXY+GLdsD8OrZE3oOHUfB4GIsmDyaAzu30OS7DsZxkxMT2LF2CWNmL8PSSsbODSv4fcsa6n7zHeGnjjF3wx5EIhFp2cjGzSvhrP7FNPiFuaUlkxat/+i1P4q4TWx0JCXKV86iTHn7BnDxzHEq1qxHbHQkj+7dJi46EpFYjKOzq7Geg7MrcTGGTaRH/gJcPH2M0pVrcO74QWKjIz96HgL/LZ5cP4e1nRNNBxv2MhlKw36uaO2WlP2mCwAHFo7h8dXT+BQ35BcUS81oM2EtVw9s4rdZg2gzYR0W1jasHtCMYvW+AwyucbW6jMY9IIRDS8dx/cg2SjRoZxw3PSWR8F0raT58AWaWVlz8bQ1X9m0gpNa3PLx0gvbTtyMSiVClme7n/r8b3hvMLCxpFbryvWtVpaXw6MppitU17CsTIp8BsCW0E3qdjrLNuxjzVr0h4txhitczJNe2Utih02qJenQbV59A7ocfJSUu6iOe8teLoEzlgLmrD3GHFhJ3eDEy/3JYeRlOvtIfXyXx7Cb0ahXa9BTMnfMblSnZnz/NXX0wd/FGqnAEwMzeDU1yNGJLa6Q2LljlKwKAdXBtki7syKJMqV7cJjPmKS9X9gZAr1Vj6RGE2FKOSGpOzJ6pyPzLI/cvZzJnq/zF8ey+4ous37Z0M2xLNyPlxmESTq/FtekIVC/vgFiM98Bf0apSeLWqD1Y+JRFbKkiLCMOr32bEltZEbhtLyvVDKIJrf5G5CPz38PH1Z+HsaSyZN5NylaoQXMxwqnb1Ujib16xApVKRkpyEt4+vUZl689PH1w/vAr44OjsD4J7Xg+io11grbHBxzUORosUBqFWvETs2r8+iTN2+cY0njx7S+0eDa6tGrSYouChya2vMzS2YNm4U5SpVpVylKiZzLlaqTBbXvY/l2KF9jBg/1XhdvnI1atRtgLm5OXt2bGHy2BHMXrIqhx4Eviby+fixZsEM1i2aRYnyVQgMMVg0b10JZ9fGVWRmpJOanIxn/gJGZapUhWrGtp75C2DvZJANV3cPYqMjkVsrcHLJQ8HgYgBUrt2Qfds3ZFGm7t26zosnjxjV06B8adRq/AuHIJNbY2ZuzsIpYyhRvgolypvKRuHipZmxavtnrVun07F6/nR6jzCNpla9fjNePHnE0C6tccrjRkDhEMTinGNo9Ro2jhVzp7B9zRJKVqyG1Mzss+Yn8O/FybMApzbM4fSmX/ApVpG8BQ1y8Pz2ZS79vhZNhgpVWjKOHj5GZarAnz+dPH1x9PBBbm/wtrF1yUtKXBQWcgUKR1fcAwx7w0IV6nH14JYsytTr+zeIe/mILT93AkCn0eDmVwQLmTUSMwsOLxuPT7GK5C9WyWTO77rhfSw6rYb980dStE4rbF08ANBrtSRGPafFqCWkxkexbXxX2k7ZjKVcAUBaQixxzx/gFWzYU4pEIur1mcjJ9bPRqjPxKlIW0Qdk7WtHUKZywNzRE49uy1DeP0/88RVY5S+OXYXviN03G48uS5HauhB/YhV6TaaxjUhq+GctEokQSczfdiYSo9f9mWTundPpbC5Br0dWoASuzU0zU3t0Xozy8RXSbp8gKfxX8v4wJ0v5l7RMvcG6cA1i984GIPXGEWQFSiOSSJHK7bH0LEzGq7uACDM7NyRyO0ObQpVQPb8pKFMC78XTy5tlG7Zz/swpViycR/FSZWn9QyfmTBnPknVbccnjxqol88nMzDC2MTM3yJRILMbM7K18icRitFqDfIlM5MtEwChZtjxjJs0wmdOitVu4En6Ok0cPsXPLRhMF51MsUw/u3UWr1RJQ6K1Lra2dnfH3Bk1bsGTuzGzbCnyduOfzZtqKrVw9d4rNy36hSIkyNGnzI8tmTWDqsi04ueZhy8qFZGa+ffdI35EN6buyIXorG6bvHlPZCC5ZjgGhphamKUs3cePyec6dOMyBXzcROjfrod2XsEylK9N4/vgBY/saDj8S42OZOqwPQ6f8gm/BIDr2HWqsO6JHW9w8vbFW2BAX8/bUPD4mCkdnQ2CXvF4+jJm1FDBY5a6cO/VR8xD472Hv5sX3E9fx+I8znN22CM+gUpRs2J7jq6by3YQ1KBzzcG7HUjTqtzIleaN8i8RIpO/s58Sit/s5PixT+YqUoX7viSZzaj1uNc9vXeR++FH+OLSNFiMXZSn/FMvUkRWTsM+Tz2hlArB2cCGPbxASqRRbl7zYu+UjMfIZeQoY3kn3LhymQMmqSKRvVQJ3v2BajlkGGNwU31i3BLJHUKZyQJMSi9hKgSK4NmJLa5Kv7DUqTmKZLbpMJam3T2IdaHpKl2O/SVGont/E0rMwqTeOYOkZnKXc0iOI2H1zUMe/wMzBA11mOpqUWKQKR3TqDOR+ZbH0LMyzed+Z9P2lLFOZcS8wdzScaijvncPMwfC71NaV9CdXUITUQZeZjurFbWzLfotenYHq5W10ahUiqQXKx1ewdAv47HkI/HeJjYlGYWNL7fqNsba2Ye+u7UbFydbOHqUyjZNHD1GlRu4U8qjI19y6/gdBwUU5cmCv0Ur1hsAiIcyZMp4Xz5/i4elFerqS2OhoHJ2dyVCpKFuxCoVDitOmiem4n2KZOnpgHzXq1M9yLy4mxmhVO3vyOPny++SqT4H/NvGx0VgrbKlcpxEyaxuO/r4D9Z+yobCzI12p5PyJw5StWusDPWUlNuo1ETf/IKBwUcIO76NgcFbZ8AsMZvmsibx+8Qw3j3yo0pXEx0Rj7+RCZkY6xctVJqBIMXq1Mg0s9CUsU3JrBat+P228fjfKYIYqHb1ej6WVjGsXzyKRSPDMXwAAmdyae7eu4RcYzIkDe6jf3LCRTEqIw9beEZ1Ox/a1S6nVpOVnzU/g30tqQgyWchsKVayPhUzBzRO7jYqTlcKOTJWSB+FH8S1dI1f9psRF8ur+ddz9grl79iDu/lm/BczjW4Tjq6eRGPkcuzyeqFXppCZEI7d3RpOhIn/RCrj7h7BqQFOTvnNrmTq7dRGZylRqdc4a2KhAySpEnDtEUJXGpKckkvD6GbYueY3lEWcPUaFVryxtlEnxyGwd0KgzufT7Gko3+RGB9yMoUzmQGfWIuMOLQCRGJJHg1GAgEksFiuINeb6oA1JrByzz5l5hMHPMR9LFXUTvmYq5szc2pbJ+1CeR2+HcdDhRO8ah16gBcKjeGbG5jMjNI/5U6PQ41u6VTe+5I+7wIlJuHEWvVvFkVgtsijfAoWpHksN/Rfn4MiKxFLGVNS5NhwNgW7op0bun8GzhD6DXoyhaDwtXwwvNulAVXizpAmIJFm6+2JRo9NnzE/jv8uj+PRbPnYFILEIqNWPA8DEoFDY0aNaCji2b4ODk9ElhzD298rNz60am/jwKb58CNGnROku5nb0Dw0InMX7EYNR/nux36tkXmUzOyJ96k5mRgV6vp+eAodl1n2tOHDnAlLmLs9zbsXkdZ08dRyKRorCxZVjopC8ylsB/g2cP77N24UzEYjESqZQuP41GrrChZqPmDGzfDDsHp08KY+6ez5sDv25m4ZQxeHgXoE7TrMqFrb0DvUZMYM7PQ4yy8V2XPljJ5Ewd0ZfMjAxAT4fegz97jesWzuL0kb1kqFR0/aYGNRo2p9WP7w+olJQQz4SfuiMSi3BwcqHvqMnGss4DR/0ZGl1FsbIVKVbW4DIVdmQ/B37dDECZKjWoXt90wyrwdRD7/AFhG+eBSIREKqV6x2FYyhUUrtaUdUNbI7N1/KQw5vZuXlw/vI3DS8fjmDc/wTWzpriQ2dhTu9tY9i8YiVZt2M+V+7Y75pZy9sz6Ca06E71e/1nBJwBS4qII370Se3dvNow0pOQoWrslhas1xSu4HE9vXGDt4JaIxGIqtemHlcLgHZEU84qU+Cg8CmU9WLm8dx2ProaBXkeRGs3xDBK+gc8Jkbu7u/6fnsTfwflLVxl7UURkqvbDlf9C1Imveb1xOPl6rv5H5/G/yneF5cQdW8n06abuIgL/2zRv3pwx4yailsr+sTm8fvWS4f17sHrrnn9sDn811mYiVi7+hWnTBBn5N9C/f3/ade9PTPo/+6qNfv2SyUN7M3tt7r/5+9rxtMykft3aPH369J+eylePu7s7B4+eYMu9f3YvBwZFZM+MAbSbuuWfnsr/JLXyahjZvwdhYWH/9FT+coQvygQEBAQEBAQEBAQEBD4BQZn6mzGzcxOsUgICfxFu7nn/01YpAYFPxcUtr2CVEhD4gtg6uwtWKQFAUKYEBAQEBAQEBAQEBAQ+CSEARS54NKkuPiMO/K1jqhNf83xBe8wc8xmj9EXvnkLavXNI5PZZrFza9GSitoeiSYxEapcH1xY/I7FSkP7kKpGbRyK1MyQFlReqZEw6/D6Swn8l8fx2NAkv8R68G4nM8LFiyvXDJJ7ZCOgRmctwbjAQizy+ACSe20ry1b2ACAvX/Dg3GYZYakH07qlkvI4AvR4zR09cmg5DbC4j8dxWks5vQxZQAef6n/fxpcB/h7oVS3Ag7PLfOubrVy/5oUVDPL28WbFpJxkZGfTr0h51ZiZarYYqNWrTsXsfACaPHcG1KxeRW1sDMCx0En4BhT44RlpqKj9824iKVWvQf6gh2lLEnVtMGTuCjAwVZStUps/gEYhEIhbNmc6hfXto1bZjlhxZAl8nbWuXZv2h8L91zOjXL+nftgnu+byNUfrSUpJZNDWUZ4/vIxKJ6DlsHAGFixrb7Nm8hrULZrDyt1PY2NmTmpLEwsljiHz5HHMLC3oOG0c+Hz8AenxbByuZDLFYglgiYdrynE/401JTmDd+OLFRr9FqtTRu/QPVGzQzlivTUunfrgmlK1Wn84CRAGxcOo+TB/eQlpKc7fM7f+IwM0YPZMqyzfgWDOL2tcssnT4OkVgsWPH+wyz4sTK9Vv69YfKTYl6xdnBL7N3yGSP0Xdm/kZvHdyESiXD09KV21zFIzS04uDiUl3evYm4lB6B2t7G4eAegSkvm8NLxJEW9QGJmTq2uo3Hy9M1x3D8ObeXqgU0kRb2g2+LDxsATGcpUDiwcTUpcFDqthhIN2hJUpTHRTyI4tmoqmempiMUSSjXpSEA5Q2TbpOiX7Js/ElVqEi7eBanbcxwSqRlX9m/k6v6N+BSvTLUOQ/7Cp/i/i6BM/QuQ2ufNEu5cUbQetqW/IWpn1ghgiWEbsMpfAvuK35MQtoHEsA041uoOgGW+YNzamObHeR+WnoVx9y/Hq9VZlRwzezfcO8xDYqUg7f55Yn6fgUfnxWiSY0gK34Fnz7WIzSyI3DaW1JvHsClaD6e6vRFbGP4pxB6cT1L4Tuwrfo9duZaIrRRkvIr41EcjIPDFcPfwNIY9Nzc3Z9bilchkcjRqNX06taV0hcoEFTGEve3ebxBVa9bJVf8rF80j5M/ExG+YPXkcg0aPI7BwMEP7diP87GnKVKhMj/6DsbSy+jILExD4RFzzemYJd75y3lSKlqnAoAmzUKvVZKrSjWWxUZFcCz+Lk6ub8d6va5fj7VeQIZPm8vLpI5bNmkTo3OXG8tC5K7Gxs/+ouRz4dTMe3j4MnzqfpIR4+n3fiEq1G2L2Zy6gzcvnG5Mbv6FkhSrU++Y7+rRpYNJfujKNvdvX4xf4NjVJYEgJRkxfyOShvT9qTgICucHONa9RkUqNj+aPg1toP20LUnNL9s4b/mf4ckMU5Erf9cWvTNYw7Rd3r8I5nz+NBkwn/tUTjq+eSvMRi0zGeRd3/xDyF6vI9gnds9y/dngbDnl9aDJoNsrkBNYMakHBCvUws7CkTo9Q7PPkIzUhho2j2uEVXA5LuYKwzfMpXq8NAeVqc3TFZG6e2E1IzRYUr9cGC5mC6Md3vuDT+nfx1br5xR1ZQlL425On+BOrSDy7GV2mkldrB/B8SWeeL+pA2l3TKCTpT67yeuMw43XMvjkk/7EfgIxXEbxc3ZfnS7vwav0gNClxX3zuVl4hiK0UJvfTIs6gCKkLgCKkLmkRnx5BxcLNHzM7N5P7lp6Fkfw5tqVHEJrkGGOZXqdFr8lAr9OgV2cgVRiyhb9RpPR6PXp1Bv8/yZ3Af5cl82axc+vbPBmrlsxn89qVKJVpDOzekS5tmtOxZRPCThw1aXv1UjjD+vUwXs+ZOoH9ewwyG3HnFv26tKfr9y0Y3KsLcTExJu0/B5FIhExm+LvVaDRoNJrP+quNuHOL+Pg4SpYtb7wXFxNDWmoqQUVCEIlE1GmQ/XMQ+G+xfvFs9v+6yXi9ZeVCdm9aTbpSSWi/zgz+sSUDf2hG+OljJm1vXr3IpCFvU2Isnz2R4/t2AfAw4hZjendgSKeWjB/YjYTYLysTaakp3Ll2mRoNvwHAzMwMucLGWL76l2m06zkwS9LSF08eUrh4acCQQDcm8iWJ8bGfNL5IJEKlVKLX61GlK7G2sUUikQCGtSfGxxFSqnyWNv5BIdg7OWfb3+bl82nappMxEbjAv5Owzb9w7dBW4/W5HUu5vHcdmSolOyb1YMPItqwb2pqHl06atH1++zK7pw8wXh9fPY1bJ38DIOrxHbaN78rGke34dUof0hI+7e82J3RaDZrMDMPPDBXW9tn/rb4h7uVjPIMMB3IO7t4kx7wmLSnnPaaLdwC2zu7ZlqlVaej1etQqJZbWNojFEuzdvLDPkw8Aa3tnZDYOpKckoNfreX7rIn6lqwNQqHKDbJ/p18pXa5myDqpG7MH52JY2uAmk3jqOe9sZiKTm5Gk1AbGFHK0ykRfLeyILqJBNVmtT9FoNMfvn4tZ6EhK5Hak3jxF/bBkuTYZlqZdy/TCJZzebtDdzyEueluM+eU3a1ASkCkcAJNYOaFMTjGWqF7d4vvhHJApHnGr1xNwl/yeP84aUq3uR+ZYBQGrjjF251jyd3RKRmTmyAqWQFXiblyB692SU9y9g5uyFY53Pz48l8O+geu26zJ85hWYtDUk0Txw+wPT5yzA3t2D8jF+QW1uTmJBAzw6tqVCl+kfJmUatZt60iUycNR87eweOHdrP8oVzGDo2a4b5w/t+Y/M60yzxeT29GDdtzgfH0Wq1dG3bgpfPn9GsZRsCi7xNxrhi4VzWLltE8dJl6dpnIOY5bMh0Oh0LZ09j5PipXL5wzng/JiYKZ1dX47Wzqysx0dEfnJfAv5vy1euyet5U6n1jSLp+7thBRs1cgrm5OUMmzUEmtyY5MYER3b+nVMVqHycTGjUr5kxm6KR52No7cOboATYum0ev4eOz1Dt16Hf2bFpt0j5P3nwMmjArxzGiX7/Exs6eBZNG8eThPQr4B9Kx31AsrWSEnz6Gg7ML3r5Z8y56+QZw4eQRAkNKcP/2DWKiXhMXE4WdgxMikYjxA7shEkGtJt9Sq/G3OY5fr/l3TBnWhy5Nq6NKT2NA6AzEYjE6nY4182fQb/Rkrl86/8FnBfAo4jax0ZGUKF+Z3ZtWfVQbgf9N/MvW5uS6mYTUNuRMu3/+CM2GzUNqZk7D/tOxkFmTnpLI5rEd8SlR+aPkSavRcGLNdBoNnInMxp6Ic4c4s20htbuOyVLv7pn9XPp9nUl7O1dPGvafmuMY1g4ulGjQlhV9GyE1tyBfkTJ4BZc1lp/dtpALO5fjGVSKCq17IzUzxzmfHw8uHidvwWJEPrxFcmwkqfHRyG0dP+ZRZaFo7ZbsmfkTy3rXQ52upH6fSYjEWe0rkQ9vodWosXPxQJWahIVcgVhiUBsUDi6kJQjvqzd8tcqUhZs/2rQENCmxaNMSkVgpkNq6oNdqiDu6DNXTayASo02JQZsWj9T6w3+smXHPyIx+zKt1Pxlu6LVIsmmnCK6FIjh3metzi0gkMhqALNz88eq/BbG5jLT754ncMpJ8fT4+q3Z2pD++QvLVveTtOB8AbXoKaRFhePXbjNjSmshtY0m5fghFsMHX1qXJcPQ6LbH75xrc/4rV/6zxBf4d+BUMJCE+ntiYaBIT4lHY2OKSxw2NWs2yBXO4fuUSIrGI2Jho4uNicXzPKfK7PHv6hMcP7/NTz04A6LS6bNvVqt+IWvU/PXG0RCJhxaadpKQkM/qnvjx6cB8fXz+69h6Ag5MTarWamRPGsmn1cn7o+v5ko7u2baJshcq4uOb55LkI/Hfw8S9EUmI88bHRJCcmIFfY4OSaB41Gzcalc7n9x2XEYjHxMdEkxsdh7+j0wT5fPXvC80cPGD+wKwA6nRY7R1OZqFy7IZVrN/ykeWu1Wh7du8OP/YbjHxTMyrlT2LlhBd+07cyv65YzetYSkzbN2nZi1dwpDOrYgnw+fuT3K4hYbLAmjV+wBkdnV5IS4hg3oCt58+UnsGhJkz7e8MeFM3j7BhA6dwWRL58zfmBXCoUU5+SBPRQvWwlHl4+TL51Ox+r50+k9YsInPQeB/y1cvANQJieQmhBDenICFnIFCsc8aDUazm5dyMu7V0EkIjU+BmVSHHK7D8tTwusnxD1/xK+TDQe/ep0u23YFK9SjYIV6nzRvVVoyDy+fouOc3VjIFOydN4w7YfsoVLE+FVv1RmbniFaj5uiKSVz6bQ1lv+lCyUY/cHLdTNYPb4OTpy8u3v6IRJ/mYPb0+nmcvfxpPnIRSVEv+HVKb9wDimIhM3wLnJYQy8FFY6jdLdREyRIw5atVpgCsA6uSevsE2tR45EHVAEi5cRitMhGPrssQSaQ8ndMKvSYza0OxBPQ646WxXK/H3MUbj045+7D+VZYpibU9mpQ4pApHNClxSOQGX/Q3bnYAcr+yxO6djVaZaAwqkVsyoh4S/dt03L6fhkRmC0D6o0uY2bkhkRv6tC5UCdXzm0ZlCkAklmBduAaJZzYJytRXRNWadTh55CDxcbFUq2VwQz28/3eSEuJZun4bUjMzWjWsSWZmVjmTSCTo35GzzIwMwy96Pd4+vixcvYmc+FzL1BsUChuKlSxN+NnT+Pj64ehs2KSam5tTt3EztqzL+WT79vU/uH71Mru2bSJdqUSjUWNlJaPFd+2IiYoy1ouJisLZxeWj5yXw76Vc1dqcO3GYxLhYytcwyMTpQ3tJTkxg2ootSKVm9Pi2DurMjCzt/r9MqDMMMqPX6/HMX4BJizfkOO7nWKYcnV1xdHbFP8jwjVHZqrXYtd6g2ES/fsmgji0AiIuJYkinlkxeugl7Ryd6/am06PV6erasi6u7h7E/AFt7R0pXrsH9OzdzVKaO79tF07adEIlEuHnkw8UtLy+fPibi1jXuXrvCwV1bUKUr0ajVWFrJaNt9QLb9pCvTeP74AWP7GgK7JMbHMnVYH4ZO+QXfgkE5PgOB/038y9Tg/oWjKJPi8C9rOKi+e2Y/yuQEvpuwDolUyop+jdGos75jxP9PnozlenDw8KH1z6bvj3f5HMvUs5vh2Dq7I7Mx7NN8S1Xj9f3rFKpYH7m9QXGTmpkTWLkRV/auB8BCZk3tbv/H3l0HVH3vfxx/nqAbBEkTMbB7CmKLigrS6827cLs/53bvwnX3vXd11/OuEQTB7sbuwMZAFATp5tTvD6aTYaDE5xz4PP7ajl/O5wX49Xven3ytJqLBwNzZ03Bw87ppOzeStmkxg6Y8gEKhwNHdB3tXTwqyzuHe2Z+q8lJSPp7NsMgn8OjSCwBLWweqykrQ67QoVWpK8nOwcZLPqytadzHlP5qcxR+hLy/C88HPANBXlqGydkShUlNxZi/aouw6X2fm4E517jkM2mr02ioqzuzBsl0vzNu0Q1dWROX5w1j69MSg06LJO19nSl1TjUxZ+w2n5MAKnALuoeTACmy6DgdAW5qHysa5Zs75haNg0KO0qimCLv78NG6hL6K2v/WIAICm6BLZ8a/QNuwlzF18rr6udmhL5YUj6DWVKNQWlJ/Zi6VHVwwGA9qCC5g5e2MwGCg7vgWzNu0a/XuXjNeo8cF8/PZrFBUW8Om3PwM1O9s5OjujNjNj364dXMq6WOfr3D08OXs6nerqaqqqKtm7azu9+vbHp0MHCgvySTu4H//efdFqNJzPOEvHzl1qfX1DRqYKC/JRqdXY2dlTVVnJ7h1biX3gb0DNWicXV1cMBgOpG9Zebffo4YMkJ/zOi2/W3ujl5Xc+uvrfyxclc/xoGo/NegYAG1tb0g4doEfP3qxcupDp0ffcUV7JtAwfE8xXH75OSWEhb35eU4yXl5Xi4OiMWm3G4b07yc2ue0+4tvUk8+xpNNXVVFdVcmjvDrr17odnu44UFxZw/PB+uvbsi1arIev8OXw61t7pqyEjU04ubXBxc+dCxhm82nXk0J4deHfoTPvOfsxd/OfaiZmRE/jgu3nYOzpRVlKMuaUVZmZmrFmcRPc+A7C2saWyombtk5W1DZUV5RzYtZXIB2sWyC9Pqpk1MTH87lrtt2nrwaE9O+jRZwCF+Ze5mHGWtp7ezH71zw+t65elkH487YaFFICNrR3/W7L56v+/+n8Pcf8qOY4ZAAAgAElEQVST/5SFlAnzGzqONd+/Q0VJEZEv14yQVleUYu3gjEqt5nzabkouZ9X5Ovs27uRdOINWU422uorzabvw9OuDk2d7KkoKuHjyIJ5deqPTainMPoeLd+daX9+QkSk7F3eyTh1CU1WJ2tyC82m7aNuxZlfYsoLL2Di1wWAwkL5nAy4+nQCoLCvBzMISldqMw+tT8O7W7+pIUtK7M5nw+BvYOtevwLFzcScjbRde3fpRVpRHQdY5HNy80Gk1LPnkWboHTKq1AYZCocCnx0BO7lxH17vGc3TTUjoPGHFH33tL1KqLKXO3jhiqy1HZt7m61siu91iy4uZw/qsHsfDset0P/moHN2z9R3L+qwdRO3pg4V7zYUqhMsM96g0uL/8MfVUZBr0OxyERjbI+6VqXkt6g4ux+dOVFnP13BM4jH8K+/2ScAu7mUuLrlOxbitrBnbaRrwNQdmQjRbsXolCqUKgtaBvxGgqFAoNBjyb/Akor+zptFO5IpHDLPHSl+Zz/6mGsuwzFbepzFGz8CX1FEblL/1PzPStVeD/6LZbePbDtHkTmN4+AUoWFhy/2A6YABnJS3qv5eRjAwr0zrpOfadSfh2TcOnbuQnlZGW1c214d1Rk7MYQXn36Ch6Km0bWHP+06dKrzdW7uHowaF8xDUVPx8PS+uv24mZk5b3z4CZ9/9C5lpaXodFoiYu+vU0w1RN7lXN57bQ56nR69Qc+oscEMGzESgLdffo7CgnwMGPD168YzL9b0FF7KzsLCwuK22pn9wiu8//qLVFdWMXh4IEOGy4dTa+DT0ZfK8jKcXd2ubpAQOG4y77/wd555IIzOXf3xal/3udGmrTvDRk3g6QfCcPPwokOXbkDNZhD/eOvfzP30PcpLS9HpdEyOvLdOMdVQM2bP4dM3X0Cr0dDW05snX3zrptdnnjvNF++8jEKhwLtjZ5544Q0Aigry+PDFmp1idTodgeMm0W9IAAAXMs7QrVe/Ou8V8eBjfPHuyzzzQBgGA9z7+Oxb7gT4y5f/ZvOapVRVVvLo9DGMCQkn+uEbT8mVTJOLd2eqK8qxdXK9OqrTbfhEFn78DL88H0PbTt1x8uxQ5+vsXNzxGzKWX5+Pwd7VE9f2fgCo1GZMnvU+G3/+F1UVpeh1WvoFx9YpphrCw7cnXQaP4feX7kWpUuHavis9R9es4V/+5StUFBcABlzb+zH64TkA5F88w6qv30ChAGevTox79BWgZhpiYXYmlrZ1P8vtWzGPPUt+oawoj19fiKVD3+GMe+RlhoTNYNXXb/DL8zGAgYCYv2Nl58jR1GVcOLaPipIijmxaAvy5NXtA7N9Z9vlLbJ3/FW7tu+I/clqj/TxMncLT09MgOkRz2L57H6/tUpBdqhMd5bZoCrPI+n1OrfOkGktVzmlK9i2jzQRx28AW719O1cXjV8+Ziu1pQ966uXz00YfCMkl3Jjw8nFfffAeN2lp0lNuWdfECc2bP5MeERXf8Hl998hHjJ0+lc5eut764Hv73zRdYWVnXOWfK1kzB3K8/58MP5T1iCmbPns19j88mt8K0HrU5WRd47/m/N/t5S+8+9yTPvvPJ1S3Pm9v1vm8fy2omBY/n3LlzQjJJf/L09GTl2g3EnzCtz3JFuRdZ9PHT3PfBzc9TuxOXz58ibeNigu698YhsU0vbuJicM0drnTM1zkvLS7Nnkpp65ztLmwq5qszIKRQq9FWlnP96RqO/t4VbJ6GFVOG2BApTf0NpYXofvqWWRaVUUlZayozYsFtffAMzZz/baIXUV598xOpli7GykveGJIZSqaK8rOTqWqjm8uKH/xVWSB05sIf3X/g7dg53tp5Ykm5EqVRSVV7Kr3PuvvXFt6mNj6/QQmrv8t/ZvfjHq4cMt0atepqfKVA7uNHh6cRbX2iCHO+KwvGuKNExJAk3dw/mL6t7ro8oM2c/y8zZz4qOIbVibdq6803SGtExmlWPPgP490/NOxIntQ52Lu787fOlomM0if4T76b/xMYvEk2JHJlqgCsH/RqLrHkvkfn9zFqvFe9fjrbkz8PmchZ9SHXu2Qa3Vbx/ObnL6r8jmiRdz5VDfI3FS8/8nZkPxNR6bfmiZC7n/nmexodvvsLZ06ca3NbyRcl88oHcnllquCsH/xqLD+bMYs5jtTdTWb8shfzLf95HX73/GufPpDe4rfXLUvj+P+/c+kJJuokrh/0ai0X//ifzXn2o1mtpGxdTWvDnYdyrv3ubvMzTDW4rbeNi1v8op403hCymWghdZQlVWcfRV5WiKfhzF6iS/StqFVNuU5/D3LWDgISSZNxKSoo5cewIZaWlXMw8f/X1FUtSahVTz736Fh06Ne7CfklqKcpKijl94gjlZSVcuvjnfbR++ULyL//5QXDmC2/g07HxFvRLUktRWVZCzpmjVFWUUpSTefX1I5uXUHZNMTXukZdx8a67eZPU/OQ0v3oqObCCwq3xoFBg3rYTbcNervXnxXsWU7x3MQadFjNnL9zCXkJpZklp2nryN/6EQqlEaWGD10OfU51zhpyF72PQacGgp23UW5i7eDcoX9nRTdj4DUNl40zp4bU4Bd5H6ZENVF08Ts6Ct1GoLfCa8SVZvz2Hy/iZVF08jib/Im3G14xkXbsRRNa8l9AW52DQVuM4JBz7AVNrrtm3rGaNk6Ut5m07o1CbAzWbZOQs/AB9eREqG0dcp72AmUPbBn0/UsuzcslC4n/5HwqFgk5d/HjprdrncCxZMJ/FyfPRajR4+bTjxTffx9LKig2rV/Djd1+iUqqwsbXls+9/4Uz6ST5442U0mmoMegNvfvQJ3u06NCjf5nWrGRY4EicXF9atWsa9Dz/GhjUrOX7kMO+8/BzmFhZ8+b84npv1GDNnP8vxI4e5mHn+6nS8K9uez37+ZV565u/kXsqmurqK8Nj7mDI96o9rFvDb/77D1s6ezl26YmZecw9lXbzAh2+8TFFhAY5OTjz/2ju09fBs0PcjtUwbVixiUdyPKBQK2nf2Y9Yr79X689WLElmzOBGtRoO7dztmvfwuFpZWbF2/kvn/+xqlUom1rS1vffET58+c4r/vvYJWo0Gv1/Ps2//Bw6d9g/Jt37SWgcNG4uDsQuqaFYTf/wjb1q/i9PE0PnvzBcwtLHjn6195558zuf/Jf5J+LI1LF85z/5M1h91f2d78b0+/xAdzZpGXk011dTWTI+9h3NRIANYtTSb51x+wsbOjfeeumJnXrLHKybrAl++/SnFhAfaOzjz54lu4tvVo0PcjtTxHNi+tObtJoaCNjy/BT9Q+3/PQumQOr09Gp9Xi2NabCTPfxMzCkhM71rBjwXcolCosrGyJfPVb8jLTWfXNm+i0GjAYmDz7A5zcG3b8S/qu9XTqH4i1vQvHt61m8LSHOLljLTmnj7Liy1dQmVkQ88Zckj98ihF3P8Wl00cpyskk8O6ngNqbQSz69z8pzbuEVlNFv+AYeo2e/sc1i9i16CcsrG1xbdcFlVnNs6go9yKrv32LypJCrOwdGffoa9i3kQfO34ospuqhOucMBZt+wWvGf1FZO6KrKK5zjU33EX9sBQ55676nZO9SHIaEU7DpJzzv/Qi1vSu6yhIAinYvxGFIBHa9x2HQaTDo9XXeLzvxdTSXz9d53fGuSOz6BNd5vfTwWpxGPIDK1plLCa/gFHgftj1GUrQzGZfxM7H07FbretvuQWT+8MTVYqo0bR1OgfcB4DbteVRW9ug1VWR+9xg23YMw6LQUbPgf3o9+h9LShos/zcb8jy3hLy//FLs+wdj3DaZ431IuL/8Mjxg57UL605n0k/zyw9d8Mfd3HJ2cKC4qrHNN4OhxhEyv+bD0/ZefsmxhEtNj7uWn777ioy++w9WtLSUlNffeoqR4wmPuZdykKWg01eh1de+hN154hoxzZ+q8HnXPg0wIqbul69qVy3jgkZk4Obfh1Wef4t6HH2Pk2AkkJ/zOzNnP0q1Hz1rXB40ZzxMPxl4tptavXs69Dz8GwPOvvY29gyNVlZU8dn8UI0aPR6vR8L+vv+Db3xKxsbXl6ccewveP7d4/+/AdJoRMI3hKKMsWJvHZR+/yzr+/uJ0fsdQKnD9ziqSfvuWdr37B3tGJkuKiOtcMDRrLuKk1m0bEffcZa5csYFLEPST++DUv/+trXFzbUvbHfbQqJYFJEfcwYnwIGo0Gvb7uDmn/fu2fXMw4W+f1kOj7GRk8tc7rW9YsI+LBx3F0cuHjV54m/P5HuGvUeJYviLvueU5Dg8by0sx7rxZTW9atIPz+RwF4Ys5b2Nk7UFVVyQuPxDI0aBxajYaEuV/ywffxWNva8fqsh+noV/N8++GT9xgZPJWRE6exdmkycz95j+ff++w2fsJSS5eXmc7OlLlEv/4DVnaOVJbWvYd8B42i1x/blG9N+Iq0DQvpOyGaHQu+J+z5z7F1dqOyrObz3MG1C+gXHEO34RPRaTUYrnMPLf1sDgVZdXeC7D/pHnoETq7z+vFtKxkS9jesHZxZ8snzDJ72EF2GjGH/6gRG3P0UbTv1qJ138GjiX3/4ajF1YvtqBofW7AI7/tFXsLR1QFtdSdwrD+A7aDR6rZbtSd8S+/YvWFjbkvj247h1qNk8acNPH9EjcDI9RoSQtmERG37+mKnPfHw7P+JWSRZT9VBxZi82PUaisq7Z4Ud1nXOZqnPOkL/+e/SVpeirK7DuPBgAS59e5Cx8D9seo7DpPuKP1/wp2Pwr2uJcbLqPuO6olHvE6/XOpy3NR5N3Act2vVEoFKBUU5VzGgu3Gw//qmwcMXPyoDIzDTNnbzSXM7D0qTnpumhHEmXHag411BXnoMnPRFeaj2WHvqhsan4GNv6j0OTVDD9Xnj+Ce1TN2g+73hPIW/1NvbNLrcO+XTsYOXYCjk4158LYX2e3rDPpJ/nhy08pLSmhoqKcQXfVHDrds28/3n/9RUaNDSZwdM1h1/69+vLr3G/IzbnEiNFjrzsq9dr7/653vvy8y2RmnKNX3wEoFArUajWnT52kk++Nz65ydHLG08uHtEMH8PZpT8bZM/Tq2x+ApHm/krp+LQC52dlknj9H/uVc+g4cjKOTMwCjxgVzPqPmAZt2cD9vffQpAOMnTeXrT/9V7+xS63Fozw7uGjX+6vlKdvYOda7JOH2SuO+/oLy0mMqKCvoMHgZA1179+O+7L3PXqAkMDRoLgF/PPiT9/B15uZcYOmLsdUelnnmj/h+kCvMvk5WZQffe/VEoFKhUZmScPkm7Tje+jxycnHHz9OZE2gE8vNtzMePs1XOmliX+xs5NNfdRXk42WZnnKMy7TI9+g3D44z4aNiaYrPNnATiRdoBn36k5AzFoQgi/flX/fwOk1uF82m66DBmDlV3NM8jStu49lJeZztb5X1NVVoKmqoL2vYYC4OnXh1XfvEGXIWPxHTQKAA/fXuxcOJeS/Bx8B4267qjU5Fnv1XntRsqK8ijIPo9n174oFAqUajWXz5+ijc+Np5Zb2zvh4OpF1slDOLr7UJB1Dk+/PgDsWxlP+u4NAJTkXaIw+zxlRXl4dx+AtX3NvyN+Q8dRmJ0BQNbJQ4TMrjlovlvAJDbHyc6I+pDFVCPJWfge7tHvYOHuS/H+5VSe3Q+Aa8g/qMw8QvnJbWR++yjej36LXa9xWHr1oOzkNrJ/f442If/EumP/Wu93OyNTZWnr0VeWkPFpzcJ5fVUZpYfWYjHm5nNpbXuOpjRtPeZt2mHTLRCFQkHF2X1UnN6D14wvUZpZcuHHpzBoqxvyo5Gkenn/9Rd5+1+f4+vXjeWLktm/ZxcA/3jxdY4cOsD21E08em8E3/6ayNiJIXTv2ZvtqRt5ftbj/OPF1+k/eGit97udkakNq1dQWlxMzJSaYq28rJR1K5fSyXf2TTOPnjCRDatX0K5DRwJHjUWhULBv90727NjOf//3O5ZWVjz16ANUV1U15EcjSfX23/de4bl3P6WDb1fWL0shbV/NffTYP1/lRNpB9m7bxHN/i+bD7+MJHDeZLj16s2fbJt557gke++er9BowpNb73c7I1NZ1KykrKeaJqJpnVEVZKalrlnP3ozc/UHv4mIlsXbcSr/YdGRw4GoVCweF9uzi0ezvvfv0rFpZWvPp/D1FdLZ9FUtNb9c2bTHn6I1zb+5G2cTGZR/cAMGbGHLJOHebs/lR+f/l+7n77Z7oND8bdtydn9qWy8MPZjJkxBx//QbXe73ZGpk5uX0NVWTFzZ9c8o6oryji+bdVNiymArneN58SONTh7tqfzwJEoFArOH9nD+cM7iX59LmYWlsx/+zG0GnkPNQVZTNWDVcf+ZMe/jONdUaisHdBVFNcZndJXVaCyc8Gg01J6aDVqu5pT7TX5F7D07oGldw/KT+1AW5yDvqoMtZMnjkMi0BblUH0pvU4xdTsjUyWH1+Jxz4dY+tRMQ9IUZHHxl2dwGfMISgsrDFXl1/06m24jKNj8K1XZJ3EZ+3jN91FZhtLKFqWZJdWXz1GVeQQAC68eXF7xObryIpQWNpQd2YB525qb29LHn9LDa7HrM4GSQ6uxat+r3tml1qHfoCG88s9ZRN7zIA6OjhQXFdYZnSovK8OljStajYY1K5bQxrVm3d2F8xn06NWHHr36sGPrZnIuZVFWWoKntw/hsfdxKTuL9FMn6hRTtzMytXblMj784lv8e/cFIOtCJv+YOYO/PTkba2sbKsrKrvt1gaPG8usP33LS3YPHZj0DQFlpCXb29lhaWXHuzGmOHDoAQI+effji4/coKizExsaGDWtW0vmP6Uk9+/Rj3arljJ88ldXLl9C734B6Z5daj14DhvDhi7OZEn0/dg6OlBQX1Rmdqigvw8mlDVqths2rl+Lcxg2A7Avn8fPvjZ9/b/btSOVyTjblZSW09fRmcsQ9XL6Uxbn0E3WKqdsZmUpdu5yXPv6Krj1r7qNLFzN58+lHuPvRWVhZ21BZfv37aMiI0Sz4+VvOnDzGvY/XnJdTXlqCjZ09FpZWXDh3mpNHDgLQpUdv5n72ASVFhVjZ2LBt/So6+PoB0LVnX7asWUFQ8BQ2r1pK9979r9ue1Hr5+A9k8X+eo//Eu69O8/vr6FR1RRk2jm3QabUc37oCG6eaz3OFlzLx8O2Jh29Pzh7YSkneJaorSnFw86JfcAwlednkZpysU0zdzsjU8W0rCX3+Mzy79AagKOcCC957kuFRT2BuaU11xfU/z3UeNJKdC+eSe9adgNj/q/k+ykuxsLHDzMKS/ItnyT51GACPzj3Z+PO/qCgpxNzKlpM71+LarqbDw9OvNye2raJ74CSObVmOV7d+9c7emsliqh7M3TriGHgfF358CoVSiYV7F9xC59S6xnnUw1z4/nFU1o5YeHXHUF0BQN7qr9DkZ2IwgHXH/pi39aVwy++UHFyFQqlGZeuMU+C9d5xNU5iFtugSFt5/zkM3c/JAaWFDZeYR7PpMJHfpv69uQHEtlZUd5m3aU517FkuvmrUb1r6DKdqzkIz/3oeZSzssvGvm5qrtXHAKepALPzxRswGF+5+9JG0mPkXOwvcp3Drv6gYUknStjp27cO/Dj/HUo/ejUqrw7dqdOW+8W+uaGTNnMfOBGBydnOnesxflZTUPja8//ZjMjHMYMDBg0FB8/brx+4/fs3rZIlRqNc4urtz78KN3nC3r4gUuZV2kR68+V1/z8PLGxtaWI4cOEDwllH+/98bVDSiuZWfvQLuOnTh3Jp3uPWsefoOHBbIoKZ77w0Pwad/h6vu6uLry4GNP8uRDsdja2ePr9+c6xlnPvcgHr7/MvJ/nXt2AQpL+yqejL+H3P8Kr//cQSqWSjl268feXav9diZnxd+Y8dg/2jk506d6Lij8KmJ+//BfZmecwGGqKsg6+XUn57Qc2rlyCWq3G0bkN0+975I6z5WRd4HJ2Fn7+f95HbT29sbax40TaQUZNnMa3H791dQOKa9naOeDVvhOZZ9Pp0qOmM67fkABWLUzgqXun4unTgS49au4vpzauRD00kxcfvxcbOzs6+P55UPaM2XP473uvsDDuf1c3oJCka7l4d2bwtIdIfPsxFEoVru39mPD467WuuSvycea99hBWdo64+/akuqLmHtr8+6cUZp8HDPj4D8K1vR+7F//E0dRlKFVqbBxdGDztobqN1lNR7kWKL2fj4ftnh7SDmxfm1rZknTpMjxFTWPe/965uQHEtSxt7nD07knfhNO6daz4Ptu9zFwfXJvHTs5E4ebTH3bemw93GqQ1Dwx8h/vUZNRtQtPe7+j4j73+WVd++yZ6lv1zdgEK6NYWnp6dBdIjmsH33Pl7bpSC7tO7iQMl4xPa0IW/dXD76SJ55YGrCw8N59c130KitRUdp0WzNFMz9+nM+/FDeI6Zg9uzZ3Pf4bHIrWsWjtkXysaxmUvB4zp2rO1VLal6enp6sXLuB+BPys5yxG+el5aXZM0lNTRUdpcnJc6YkSZIkSZIkSZLuQKsppsorKrExU4iOId2ClVJHWVmp6BjSHSgvLwdD3S3Kpcal02ooLZX3iKkoLy/HoNOIjiE1gLmZmrIbrJuUmld5eTlWf5wrJhk3C5Wy1dw3raaYOnzoIOM7yCVixszRUskQdxVHjhwRHUW6A8eOHcPawhy17LNoMkoFqAxaeY+YkLS0NGzUBtSt5mnbstiaQVVVJZcvXxYdRQIKCwspLimms6NKdBTpJtraqHCytSA9PV10lGbRatZM2dvbEzd/AQWWXhy4bKBa1yq+bdOgADtzJRPbK1jw21z+JddLmazw8HDee/99dCpL5C3WuJQKUGoqmPvD93K9lIl56qmneGzmk5RhgU4P8tYwfgoFmCkMWFNJZEQEhw8fFh1J+kP37t1JSEziTLk5hVVgMMg7yngosDVX0N1RzxOPPcKGDRtEB2oWraaYgpqC6sEHH6Rz1x6YmVuIjlPH8OHDOHz4MEVFxU3y/l27dkWr1ZCefrpJ3r8higry2LE1lZSUFNFRpAYKCgpi/PgJeHh6oKB5h6m69+hOeXk55842zUJxVzc32rXzYc/uPU3y/jeTl59H6ubN8h4xUVOnTiUgcAQubVya/b64He7u7ri7u7N///4meX9zc3PuGjaM1M2b0emMdxMBvV7P2TOnmT9/PsePHxcdR/oLX19foqKiad+xE0qlcQ37WlpaMmTIUDanbkbfRH/HhwwdyonjJygoyG+S979TBmqOM1m6ZDG7d+8WHafZtKpiypgNGTKE9957j9GjRzdZGz169ODHH39k6NCh6PVybYvUslhbW7Nr1y5GjRpFTk5Ok7RhZmbGrl27CAsL48yZugcCS5KpS0hI4Oeff2bJkiVN1sb333/P2rVriYuLu/XFkmRiZs+ejaurKy+99FKTtTFjxgz69OnDrFmzmqwNqf6Mq5xvxWJjY5k3b16TtnHkyBHy8vIICAho0nYkSYSQkBB27tzZZIUUgEajYcGCBURHRzdZG5IkSvv27enWrRurVq1q0nbi4+OJiYlp0jYkSQSFQkF0dHSTdxQsWLCAsWPHYm9v36TtSPUjiykjYGtry/jx40lKSmrytubNmycfYlKLFBMT0+QdEgBxcXFERESgUskF0FLLEhUVRXJyMtXV1U3azrp16/D29sbX1/fWF0uSCRk+fDglJSVNvsauoKCATZs2ERoa2qTtSPUjiykjMG3aNFJTU8nLy2vytlJSUhg5ciROTk5N3pYkNZfOnTvTsWNH1q5d2+RtnTx5kosXLzJy5Mgmb0uSmotSqSQqKqpZpt7pdDrmz58vO/akFicmJqbZpq/GxcXJWRJGQhZTRiAmJob4+PhmaauoqIi1a9fK3gypRYmKiiIpKQmtVtss7ckRXqmlGTFiBDk5ORw7dqxZ2ktISCA8PBy1Wh5ZIrUMDg4OjB49utk2Cdq8eTOurq507969WdqTbkwWU4L5+fnh4eHRrNtHxsfHExsb22ztSVJTUqlUREZGNuti9kWLFjF8+HBcXFyarU1JakrNsW73WqdPn+b06dOMGTOm2dqUpKYUGhrKhg0bKCgoaJb29Ho9CQkJsmPPCMhiSrCYmBjmz5/frFvEbtmyBTs7O3r16tVsbUpSUxk9ejQZGRnNejhgaWkpK1euJCIiotnalKSm4uTkRGBgYLNvuy879qSWpDmn+F2RkJBAWFgY5ubmzdquVJsspgQyMzNj+vTpzTbF7wqDwSB3U5JajObaeOKv4uLi5D0ktQjh4eGsWbOGkpKSZm138eLFDBo0iLZt2zZru5LU2Pz9/XF2diY1NbVZ283IyODo0aNMmDChWduVapPFlEBjx47l5MmTnD17ttnbTkhIYNq0aVhaWjZ725LUWFxdXRk6dCiLFy9u9rZ37tyJWq2mf//+zd62JDWm5tjK+XoqKipYunSpHOGVTF50dDTx8fEYDM1/dOu8efPkRhSCyWJKoNjY2GYflbri4sWLHDhwgODgYCHtS1JjCA8PZ8WKFZSVlQlpX25EIZm6Pn36YG1tzfbt24W0L+8hydRZWFgQFhZGQkKCkPaXL19O37598fT0FNK+JIspYdzd3enfv3+TnjJ/K/PmzZPz1SWTFhsbK6RH/YrExEQmT56MlZWVsAyS1BBXOvVE9KgD7N27F61Wy+DBg4W0L0kNNWHCBA4fPkxmZqaQ9isrK1m0aBFRUVFC2pdkMSVMZGQkS5cupbKyUliGlStX0r17d3x8fIRlkKQ7NXDgQAB2794tLMOlS5fYtWsXU6ZMEZZBku6UpaUlISEhzJ8/X2gO2bEnmTJR63avdeXMKYVCITRHayWLKQEUCoWwOerXqq6uJjk5Wc61lUySMTzAQB6cKJmuyZMns3fvXrKysoTmSExMZMKECdja2grNIUm3y8vLi169erFixQqhOQ4dOkRJSQnDhg0TmqO1ksWUAEOGDKGqqor9+/eLjkJ8fDxRUVEolfKvgmQ6rK2tmcYaawgAACAASURBVDhxIomJiaKjsHbtWjp16kSnTp1ER5Gk29KcB8bfTF5eHlu2bGHq1Kmio0jSbYmKimLhwoVUVVWJjiLXHwokP0EL0NyHI97MkSNHuHz5MoGBgaKjSFK9TZkyhR07dpCbmys6ClqtlqSkJDk6JZmU9u3b4+fnx6pVq0RHAeRUP8n0GMssoyuSk5MZM2YMDg4OoqO0OrKYamZ2dnaMGzeOpKQk0VGukr0Zkqkxlil+V8ybN4+IiAhUKpXoKJJUL9HR0SQnJ6PRaERHAWDDhg14eHjg5+cnOook1UtAQABFRUWkpaWJjgJAQUEBGzduJDQ0VHSUVkcWU81s2rRppKamkp+fLzrKVSkpKQQFBeHk5CQ6iiTdUufOnenQoQPr1q0THeWqU6dOkZmZyejRo0VHkaRbUiqVREVFGU2POoBOpyMxMVF27EkmIyYmxqjuIZCHyYsii6lmFh0dbVQ96gDFxcWsWbOG6dOni44iSbcUHR1NYmIiWq1WdJRa5MGJkqkICgoiOzub48ePi45SS3x8PNOnT8fMzEx0FEm6KUdHR0aNGkVKSoroKLWkpqbi4uKCv7+/6CitiiymmlHXrl3x8PBgw4YNoqPUER8fL3szJKOnVquJiIgwug4JgEWLFjFs2DDatGkjOook3ZQxrdu91pkzZzh16hRjx44VHUWSbio0NJT169dTWFgoOkoter2e+Ph42bHXzGQx1YxiYmKYP38+er1edJQ6tm7dio2NDb179xYdRZJuaNSoUWRkZJCeni46Sh1lZWWsWLGC8PBw0VEk6YacnZ0JCAhg4cKFoqNcl+zYk0yBsa3bvVZCQgJhYWFYWFiIjtJqyGKqmZiZmTF9+nSjvfkMBgMJCQnyISYZtdjYWKObo36tuLg4uSOZZNTCw8NZvXo1JSUloqNc15IlSxgwYADu7u6io0jSdfn7++Po6EhqaqroKNd1/vx50tLSmDBhgugorYYspprJuHHjOHHiBOfOnRMd5YYSEhKYOnUqlpaWoqNIUh2urq4MGTKExYsXi45yQ7t27UKpVDJgwADRUSTpuoxx0fy1KioqWLp0KREREaKjSNJ1XTmfzWAwiI5yQ3INb/OSxVQzMeYh4SsuXrzIvn37mDhxougoklRHREQEy5cvp7y8XHSUm5JHDUjGqm/fvlhYWLB9+3bRUW5K3kOSsbKwsCA0NJSEhATRUW5qxYoV9O7dGy8vL9FRWgVZTDUDDw8P+vfvz9KlS0VHuaX4+Hg5TUkySsa6aP6vEhMTmTRpEtbW1qKjSFItsbGxxMfHi45xS/v27aOqqoqhQ4eKjiJJtQQHB3Po0CEuXLggOspNVVZWsnDhQqKiokRHaRVkMdUMIiMjWbJkCZWVlaKj3NKqVavo1q0b7dq1Ex1Fkq4aOHAgBoOB3bt3i45ySzk5OezcuZOQkBDRUSTpKktLSyZPnsz8+fNFR6kXuRGFZIxMYZbRFVem+ikUCtFRWjxZTDUxhUJBdHS0Uc9Rv1Z1dTXJyclyrq1kVIx944m/khtRSMYmJCSEvXv3kp2dLTpKvSQmJjJ+/Hjs7OxER5EkALy9venZsycrV64UHaVeDh8+TFFREQEBAaKjtHiymGpiQ4cOpaKiggMHDoiOUm9xcXFERUWhVMq/HpJ4NjY2BAcHk5SUJDpKva1bt44OHTrQuXNn0VEkCTDOA+NvJj8/n9TUVKZOnSo6iiQBEBUVRUpKClVVVaKj1JvciKJ5yE/LTcxU1nlc69ixY+Tk5DBixAjRUSSJKVOmsH37dnJzc0VHqTetVktiYqJ8iElGoUOHDvj5+bF69WrRUW6L3IhCMhZKpdLkOiQAkpOTGT16NA4ODqKjtGiymGpCdnZ2jB071qR61K+YN2+enKYkGQVTmqN+rXnz5hEREYFarRYdRWrloqOjWbBgARqNRnSU27Jx40Y8PDzo2rWr6ChSKzd8+HAKCgpIS0sTHeW2FBYWsmHDBkJDQ0VHadFkMdWEQkND2bx5MwUFBaKj3LaFCxcSGBiIk5OT6ChSK+br60u7du1Yt26d6Ci3LT09nYyMDEaNGiU6itSKqVQqIiMjTbJDQqfTMX/+fDk6JQlnaut2ryXX8DY9WUw1IVMcEr6iuLiYNWvWEB4eLjqK1IpFR0eTmJiITqcTHeWOyGlKkmhBQUFkZWVx/Phx0VHuSHx8PNOnT8fMzEx0FKmVcnR0ZOTIkaSkpIiOckdSU1NxcnLC399fdJQWSxZTTaRbt260bduWjRs3io5yx+TCRUkktVpNRESESZyLcyOLFy9m6NChuLq6io4itVKmOk32irNnz3LixAnGjRsnOorUSoWFhbFu3TqKiopER7kjBoNBHjXQxGQx1URiY2NJSEhAr9eLjnLHtm3bhrW1NX369BEdRWqFxowZw5kzZ0hPTxcd5Y6VlZWxYsUKOcIrCeHi4kJAQAALFy4UHaVB5AdBSSRT3EjsrxISEggNDcXCwkJ0lBZJFlNNwNzcnLCwMJPuUQfZmyGJZeo96lfI+eqSKOHh4axatYrS0lLRURpkyZIl9O/fH3d3d9FRpFamZ8+e2NnZsWXLFtFRGiQzM5PDhw8zYcIE0VFaJFlMNYHx48dz7NgxMjIyREdpsPnz5zNlyhQsLS1FR5FaETc3NwYPHsySJUtER2mw3bt3AzBw4EDBSaTWJiYmxmQXzV+rsrKSpUuXEhkZKTqK1MrExsYSHx+PwWAQHaXB5BrepiOLqSZgyhtP/FVWVhZ79+5l0qRJoqNIrUhERATLli2jvLxcdJRGIR9iUnPr168f5ubm7NixQ3SURhEXF0d0dDQKhUJ0FKmVsLS0ZNq0aSQkJIiO0ihWrFhBr1698PLyEh2lxZHFVCPz9PSkX79+LFu2THSURhMfHy+nKUnNqqVM8bsiMTGRiRMnYm1tLTqK1Eq0tHto//79VFZWMmTIENFRpFYiODiYAwcOcPHiRdFRGkVVVRUpKSlERUWJjtLiyGKqkUVGRrJ48WIqKytFR2k0q1atws/Pj/bt24uOIrUCgwYNQq/Xs2fPHtFRGk1ubi47duxgypQpoqNIrYCVlRUhISEkJiaKjtKoZMee1JxiYmJMfu37X13ZpVmO8DYuWUw1IoVCQXR0dIuYo34tjUZDcnKy3CZdahamfDjizciNKKTmEhISwu7du8nOzhYdpVElJSUxbtw47OzsREeRWjgfHx/8/f1ZsWKF6CiNKi0tjcLCQgICAkRHaVFkMdWI7rrrLsrKyjh48KDoKI0uLi6OqKgolEr5V0ZqOjY2NgQHB5OUlCQ6SqNbv3497dq1o3PnzqKjSC1cS1q3e638/HxSU1OZNm2a6ChSCxcVFUVycjLV1dWiozQ6uYa38clPxo2opc1Rv9bx48fJzs4mKChIdBSpBZs6dSpbt27l8uXLoqM0Oq1WS2JionyISU2qY8eO+Pr6smbNGtFRmkRcXJy8h6QmpVQqW2yHBEBKSgqjRo3C0dFRdJQWQxZTjcTe3p6xY8eyYMEC0VGazLx58+Q0JalJtYTDEW8mPj6eiIgI1Gq16ChSCxUdHU1SUhIajUZ0lCaxceNG3N3d6datm+goUgsVGBjI5cuXOXLkiOgoTaKwsJD169cTGhoqOkqLIYupRhIaGsqmTZsoKCgQHaXJLFy4kICAAJydnUVHkVqgLl264OXlxfr160VHaTLp6emcPXuW0aNHi44itUAqlYrIyMgW3SGh1+tJSEiQo1NSk2mJG0/8lewcb1yymGokLeVwxJspKSlh9erVhIeHi44itUAxMTHMnz8fnU4nOkqTkvPVpaYyatQoLly4wMmTJ0VHaVLx8fGEhYVhZmYmOorUwjg5OREUFERycrLoKE0qNTUVBwcH/P39RUdpEWQx1Qh69OhBmzZt2Lx5s+goTU5+EJSaglqtJjw8vMUcjngzixcvZsiQIbi5uYmOIrUwLXmdx7XOnTvHiRMnGD9+vOgoUgsTFhbG2rVrKS4uFh2lSRkMBnnUQCOSxVQjiI6OJiEhAb1eLzpKk9u2bRsWFhb07dtXdBSpBRk7dizp6emcPn1adJQmV15ezvLly+UIr9So2rRpw/Dhw1m0aJHoKM3iynk5ktSYWvq63WslJCQwbdo0LCwsREcxebKYaiBzc3PCwsJaRY/6FfHx8XJ0SmpUrWGO+rXkfHWpsYWHh7Ny5UpKS0tFR2kWS5cupX///nh4eIiOIrUQvXr1wsbGhq1bt4qO0iwuXLjAoUOHCA4OFh3F5MliqoEmTJjA0aNHycjIEB2l2cyfP5+QkBAsLS1FR5FagLZt2zJo0CAWL14sOkqz2b17NwaDgYEDB4qOIrUQrWHd7rUqKytZsmQJkZGRoqNILURsbCwJCQkYDAbRUZqNXLrROGQx1UCtZY76tbKzs9m7dy8hISGio0gtQEREBEuXLqWiokJ0lGYVFxcnR6ekRtG/f3/UajU7d+4UHaVZxcXFER0djUKhEB1FMnGWlpZMnTq1Vc0yAli5ciU9e/bE29tbdBSTJoupBvD09KRv374sX75cdJRmJ+erS42lJR92fTNJSUkEBwdjY2MjOopk4lrrPXTgwAEqKioYOnSo6CiSiZs4cSL79+/n4sWLoqM0q6qqKlJSUoiKihIdxaTJYqoBoqKiWLhwIZWVlaKjNLvVq1fj5+dHhw4dREeRTNjgwYPRaDTs3btXdJRml5uby/bt25kyZYroKJIJs7KyYvLkycyfP190FCHk+kOpMbTWDgn4s3NcqZQlwZ2SP7k7pFAoWuUUvys0Gg0LFiyQo1NSg8TGxraqjSf+Ss5XlxpqypQp7Nq1i5ycHNFRhEhKSmLs2LHY2dmJjiKZqHbt2tG9e3dWrVolOooQaWlp5OfnM3z4cNFRTJYspu7Q8OHDKSkp4dChQ6KjCDNv3jwiIyNRqVSio0gmyNbWlgkTJpCYmCg6ijDr1q3Dx8cHX19f0VEkE9XaNp74q4KCAjZv3kxoaKjoKJKJio6OJjk5merqatFRhJEjvA0ji6k71JpHpa44fvw4WVlZBAUFiY4imaCpU6eyZcsW8vLyREcRRqfTkZiYKEenpDvSqVMnOnbsyNq1a0VHESouLk7eQ9IdUSqVREVFtfrPcykpKYwcORInJyfRUUySLKbugIODA2PGjCE5OVl0FOHkNCXpTrWmwxFvJiEhgfDwcNRqtegokomJjo4mKSkJrVYrOopQmzZtws3Nje7du4uOIpmYESNGkJOTw9GjR0VHEaqoqIh169bJEd47JIupOxAaGsrGjRspKCgQHUW4RYsWERAQgLOzs+gokgnx8/PDw8ODDRs2iI4iXHp6OmfOnGHMmDGio0gmRKVSERERITskAL1eT0JCguzYk25bazsw/mbkVL87J4upO9Da56hfq6SkhFWrVhERESE6imRCYmJiSExMRKfTiY5iFOQIr3S7Ro8eTWZmJqdOnRIdxSjEx8cTFhaGubm56CiSiXBycmLEiBGkpKSIjmIUtmzZgp2dHT179hQdxeTIYuo2+fv74+LiQmpqqugoRkPOV5duh5mZGdOnT5e9gddYsmQJgwcPxs3NTXQUyUTIdbu1ZWRkcOzYMcaPHy86imQipk+fzpo1ayguLhYdxSgYDAbi4+Pl6NQdkMXUbYqOjiY+Ph69Xi86itHYsWMH5ubm9OvXT3QUyQSMHTuWU6dOcebMGdFRjEZ5eTnLli2TI7xSvbRp04Zhw4axaNEi0VGMijxMXrodrflsqRtJSEhg2rRpWFpaio5iUmQxdRssLCwICwsjISFBdBSjI6cpSfUl56hfn7yHpPoKDw9nxYoVlJWViY5iVJYtW0a/fv3w9PQUHUUycr1798bGxoZt27aJjmJULl68yIEDBwgODhYdxaTIYuo2TJgwgbS0NM6fPy86itFJTEwkJCQEKysr0VEkI+bu7s6AAQNYsmSJ6ChGZ8+ePej1egYNGiQ6imTkYmNj5brd66isrGTx4sVERkaKjiIZuSsHxhsMBtFRjE58fLzs2LtNspi6DXLjiRvLzs5m9+7dhISEiI4iGbHIyEiWLl1KRUWF6ChGKS4uTs5Xl25q4MCBKJVKdu3aJTqKUYqLiyM6OhqFQiE6imSkLC0tmTJlipxldAMrVqzA398fHx8f0VFMhiym6snLy4vevXuzcuVK0VGMlpyvLt2KXDR/c0lJSQQHB2NjYyM6imSk5D10cwcPHqSsrIy77rpLdBTJSE2aNIl9+/aRlZUlOopRqq6uJjk5maioKNFRTIYspuopKiqKlJQUKisrRUcxWmvWrMHX15eOHTuKjiIZoaFDh1JVVcW+fftERzFaly9fZuvWrUydOlV0FMkIWVtbM2nSJBITE0VHMWpy/aF0M/LA+Fu70jmuVMoyoT7kT6keFAqF7A2sB41Gw4IFC+TolHRdcuOJ+pHz1aUbCQkJYefOneTk5IiOYtQWLFjA2LFjsbe3Fx1FMjLt27fHz8+P1atXi45i1I4cOUJeXh4BAQGio5gEWUzVQ0BAAEVFRRw+fFh0FKM3b948IiIiUKlUoqNIRsTOzo7x48fLHvV6WLduHd7e3vj6+oqOIhkZufFE/RQUFLBp0yZCQ0NFR5GMTHR0NMnJyVRXV4uOYvTmzZsn1/DWkyym6kGOStXfiRMnyMrKYuTIkaKjSEZk6tSppKamkp+fLzqK0dPpdMyfP1+OTkm1dO7cmQ4dOrBu3TrRUUzClY0oJOkKpVJJVFSU/DxXT8nJyQQFBeHk5CQ6itGTxdQtODg4MHr0aJKTk0VHMRlxcXHyg6BUizwc8fYkJCQQHh6OWq0WHUUyEtHR0SQmJqLVakVHMQmbN2/G1dWV7t27i44iGYmgoCCys7M5duyY6Cgmobi4mLVr1xIWFiY6itGTxdQthIaGsmHDBgoLC0VHMRmLFi1i+PDhuLi4iI4iGYGuXbvi4eHBxo0bRUcxGadPn+b06dOMHTtWdBTJCKjVaiIiImSHxG3Q6/UkJCTIjj3pKrlu9/bJqX71I4upW5Bz1G9faWkpK1euJCIiQnQUyQjExMQwf/58dDqd6CgmRW5EIV0xatQoMjIySE9PFx3FpCQkJBAWFoa5ubnoKJJgzs7OBAYGkpKSIjqKSdm6dSs2Njb06tVLdBSjJoup6+jXrx9z5szB398fJycnUlNTRUcyOVem+pmZmfHbb7+JjiM1M3Nzc3799VfMzMyYPn267A28A4sXL2bQoEG0bduWjz/+WB6g2Ap9+umneHp6yk69O5SRkcHRo0eZMGEC9913nzxyoBV66KGHmDhxIuHh4axevZqSkhLRkUyKwWAgISGB2NhY/P39ee2110RHMkqymLoOc3NzBg0adHVIeNCgQXh6eoqOZTKGDx/O2bNnUavVjB49Gj8/P9GRpGam1WoJCAggODiYEydOoNfrGTBggOhYJsPX15fOnTuzdOlSIiIiGDZsmDzvoxXy8PCgX79+DBkyhHXr1jF+/HjRkUyGra0t48aNu3peTt++fbG2thYdS2pmDg4O9OzZ8+q63cmTJ2NmZiY6lskICQlhwYIFTJ06ld69e8vlGzcgn87XkZubS9u2bQkNDaWsrIxvvvkGS0tL0bFMhq+vLykpKaxcuZKoqCh5JkorpNfrycvL495772XLli2kpKTg4eEhOpbJsLOz47fffiM9PZ2YmBjc3NzkfdQK5eTkMGnSJNauXcvcuXMZPny46EgmQ6VS8eqrr9K1a1f69u1Lu3btyM3NFR1Lama5ubl0794dCwsLQkNDefLJJ0VHMilTpkzh3Xff5eDBgwQEBMjn0A3IYuo6Ll26hIeHB4WFhTz00ENERERw+vRp0bFMxk8//cQXX3xBREQEI0eOJC8vT3QkSYCCggL69+/Pww8/zJw5c1iyZInoSCZj3759PPDAAzz++OPY2tpiMBioqKgQHUtqZjk5OYwcOZIhQ4awZcsWOcXmNhQVFREaGsqIESPIycmhS5cuXLp0SXQsqZnl5OTQu3dvqquradeuHZGRkWg0GtGxTMYTTzzBpUuX8PHxYdiwYbKYugFZTF1HWVkZarUaS0tLQkND5aLfO/D777/zwgsvYGZmhpWVleg4kgBmZmao1WoeffRRVq5cKTqOydm/fz8RERGYm5vL7bBbKTMzMxwdHfnuu+94//33RccxOXl5eURGRlJeXo6rq6v8INgKFRYW4uHhwblz53jggQcoKysTHcmk6HQ6/vGPf7By5Urc3Nzks+gGVHZ2dq+LDmGMnJ2dmTVrlvzHtwFOnTrFmTNnSEtL4+jRo6LjSM1MrVbz/fffs3XrVtFRTFZBQQGrVq1Co9HIjXBaofLyck6dOsU333wjOorJ0mg0JCYm4u7uLndya4WKiopwcnJi1qxZshBogE2bNmFhYcHy5cvJz88XHcfoKDw9PQ2iQ0iSJEmSJEmSJJka9e1+gZWVldxVSgC9Xn9HayZsbGyaII10RWVlZb3OT7KwsECtvu3bTWpC5eXlGAx31pckf593pqqq6rZ6h+XP2fhZW1ujUCjQarVUVVWJjiPdRFVVFRYWFqJjSNfQ6XRUVlZe/X8zMzN5LpqRqaioQK/X3/Saej+lHnx4Bs8+9zyWFubo9HIwq7mplAoqq6r59JP/8O3XX930WrVazb8++YzJkyYCcIefF6V6UKuUHD95igfvu4fs7Ow6fx4UFMTnn/wLBycX9Fq56NVoKBSgULB18yZmPPZErYfZzQQFBfGfz77AycEere7m/7hKdalVSk6fy+Dh++/l3LlzN7wuKCiIL774Ant7+1s+xCQxFEolKpWq1msGQ82HQ4P8nRkfpQoztRKd3iCnuxkRlVJJeUUlP879gaiIMNzaeqDXyd+PMVGqVKQdOsg99z9IQUHBda+p1zS/Bx+ewRNPP8dLm8vJLL51L7zUNDztVLwTYM3/vv6Ub7768rrXKBQKvv1hLj49hzD/hJ4KraykmpJKAQHeanrblTF18sRaW+8GBgbywzdfUrXiA6rPHwLk78KYKMwssRj5BCfLrYm6+16qq6tven1gYCBfffcDP6ZpOZmnkb/NO6BSwHBvM0Z5aJkWMonMzMw61wQGBjJ37g9Y6KtAJzsgjJHBwpZqpSVpuRqqZd1k1FQK6N7GnFP5GlLPV6KVvy+j4+ukZoqvGeV7F6G5eET2gBsbpRqV73DyLL2YPGUaRUVFdS+pz/s8+9zzspAyAhdLdMzZXM4zz/zjhtd07dqVgUOG8fsxnSykmoHOABvPa8nS2RIWFlbrz55/ZhaaTd9Qff4gspAyPgZNJZVrPqGLdxsGDRp0y+tn//N5kk7qOCELqTumM8Cm8xrSii2IjY297jVzXngeSzSykDJaCpQW1hzMkYWUKXC2UlJYqWfDOVlIGSs/R6g8tgnNhTRZSBkjvRbdiY04q6oIDg6+7iW3nOZnZWWFpYU5mcXFjZ7veoo2zMWiXW8sOw284TUVJ7agyT2L/fB7GtRWddZx8he9j0FbhaXvUBzH/x8KhaLWNQaDgcJVn1N5ajsKM0ucp7yAuYcfAIVrv6Hi1DYAHALux9p/NAD5iz+kOus4YEDt7I3z1BdQmjfOyevZpTpQKLC3t6f4Or8TLy8vLhVXN9k/mseWfIuLb19cuw2+ccaDmynJPkOX8fc3qK3CjGPs++UtdJoq2voPo2fE03V+P1kHN3FsybcoFEoUShU9I2bj0rkPAIv+bzj2np0BsHJqy5DHPwJgz4+vUZhxDKVKjWP77vSJfQGlqmHrMnIqVPi061DrNU9PLzSHm/98sh9259PHw5KBXjf+O7flXBlnCzTc09exQW0dz63ivY05VOsMDPGxZtZdLnV+R+cKq3l/Yy4nL1fxt0HOxPSuabNKq2fWkiw0OgM6vYGgTjY8PMAZgA825nL8chUGwMfBjBeCXLE2a4K1mgY9urxzeHl53fJST09PVpxo/A6l08u/w7FzX5z9blzQ5R7eTNmls3QYc1+D2io+f4yjce+g11Th0v0uuoTNrvP7yj28mdPLv0OhUKBQqugS+hSOnWruqf3fPEPxuTQcOvWmz98+uuMcF0oN+HTsfN0/8/DwxCB4msvn3/zAgH59GDb4xs+hdZtSST9zlkceuLdBbaUdPc6Lb75LZVU1I4YN5cV/zKrzO7ni0JGj3D3jCT5++zUmjBkJwL8+/4qNW7YDMHPG/UwcNwaAbTv38PHnX6LXG7CxtuKdV+fQ3se7QVkBUCrR6PToBH/mS/7xv/j1GoD/gKE3vGbf1g1cPJfO5NgZDWrr7Ikj/PDhK2iqq+g1OIC7n3z+hr+jM8cO886s+3n85Q8YOGIcAAnf/oeDOzZhMBjo0X/o1a//4JkZFObnYm5hCcA/3v8KeyeXBmX9KwuVgktlzX8/7V7wDe5d++Htf+PPCmf3baLwwhn6hjzQoLZyzx5lw/dvoquuwqf3MIbd848b/n5yTh9h4dszGDPzbToNqrlXln08i5z0w7j79SH46f9cvdZgMLAr6SvO7FqLQqmix+hweo6LblDW63GwMKArqrtMoKl9u+ogfTu6MbiL+w2v2XQkk7M5Rdw/0r9BbR3LzOet+duo0uq4q6snz0wZUOd3tCktk29WH0SpqJn+OHtKf/p2cAPg82X72HrsInqDgcFd3HlmygDKq7U8/vXqq1+fU1RBcL8OPD1lQIOy3ohZxeUbfla45SdIpVLZrGukHEY+fMtrrPyGY+XX8JPgC5b/B6fJ/8TcqweX5z1PZfpOrHyH1LqmMn0H2vxM3J/4jeoLRyhY/h/aPvwVFSe3UZ19AvdHvseg1ZDzy2wsfYegtLDBcfyTKC1qNn4oWP1fSnclN7jwu5ZWp68zV/0KpVLZpA+5biGP3vIa996BuPcObHBbB+M/pM/dc3Dq4M+Or54h58h22vrfVesa164Dce8ViEKhoOjCKfbMfYnRr8QDoDKzGJKmpQAAIABJREFUYOScn+u8r/egCfR/4HUA9v74Gue2LqJj4PQGZdUZDKj+slBeoVRAPTanaGwzBjrf8prh7W0Y3r7hbf17y2WeDXSlh5sFz63IZkdmBUN9ahdx9hYqZg1zIfVsea3XzVUK/jPZA2szJVq9gb8vusgQ70r821ry97tcsDGvKZ6+2JZHclpxgwu/G9Lr6rWpTs291fg3V6eJj9zyGteegbj2bPg9dTzxY7pFPY99e38OfPdP8o9tx6V77XvKqcsABvsHoFAoKL14isM/v8LQF/6fvbMMjOroGvCzvtmNbDzEhQSIkOBO8EILRUuRlgKFUipYKRQpXqC0UMFdimuhWPEQXENwDZoQgfhmfb8fC0nTRRqc98vzJ9m5947s7MydM+fMOcsA8K3bAZNew52D65+rHkYziB6zgSF4Axwcfd3j6YvverVrUq92zecua9SPExk1eABlw0Pp0WcAsQcPU7u6tYBgNBqZNHkG1asUCHgx+w5y7uJl1i6ei06vp/PnvalVrSq2tkpG/TiRKT+PJSjAn2Wr1zFz3iLGDh/83PUFwRuxed6y85dPvadc9TqUq17nucv647cxdO43nMAyEfwy+EtOH91P2crWfW8yGlk151fCKhaMqStn47hyNo5Rs1YDMK5PZy6eOkbpKMvmyWeDxhFQ6vkWqk9EwGsRfCu26vHUe/zL1YZytZ+7rH0Lf6R258G4BYWzdVIfbp0+iG/Z6lb3mUxGjqyajHd44XVe5LsfYdBqOb9nbaH0S/s2kns/mbbjViEQCsnLejkuwQUIwPzq1YafNSr71Htqh3pTO/T5N2Em/HmEQa2rEObjTN/5ezh4KYnqpTwL3VOxpDu1QpsgEAi4nJTO0KX7WfFNU+JvpBJ/I5XFfSx+AHrM2M6JaylUCHLnj97v5j//yeQt1Anzee66Pg6B2Yz4MWvv1+YmKTN2EerT2xEqHBDbuyEpEYJ9tXbc2zAOm+BqKMrUIXHyhyjLNibv8gEwGnBuPQKJix+5p7agS7qIY+M+z1y+MfseJm0uMm/LJKaIeIe8i/ushKm8i/tRRLyDQCBA5h2GSZODMfse+rQbyHwjEQjFCKRipG5BaK4eQRFaN1+QMpvNmPVay2H3t4yLW+Zx++jfyGxV2Di64+BTipINOnLyj9G4h9fAs1w9tg9riU+Vd0k+vQ+T0UDFT3/AzsOfm4c2kXHzPGXb9n/m8jWZaRg0uTgFhAPgXbkJd+NjrIQpsaxg4W7U5gFP/67dwwomWZVfGTTpb2cssYUn0tl+JQeVXIirrZhSLjLalVUxbk8K1XwV1Am05cNlN3knxJYDN9QYTGZGNnDHTyVly6VsLqZq6VPD5ZnLv6c2oNaZCHO37Kq+E2zHvuu5VsKUo40IRxsRh24WFqYEAgEKiaW/DCYzBpM5f6g8FKTMZjNaowkBj57A3iYSts0n+fjfSGwdkancsPcuhW/dDpxbNgaX0Bq4RdblwOjWeFRqQtrZ/ZiNBsI/GYPS3Y+kI5vIunWBUq0fb+L7NLRZaRi1uTj4W8aUR8XGpJ6OtRKmCo0pnYZ/jimnkIqkXznxzHV405g+dyF/bdmGk6MKD3c3QkuH0PWj9gweOZbomtV5p34dGjRvS/P3GrMndj8Gg4Ffxo0i0N+PdRu3cPb8BYZ+2/eZy09NSyMnV01khOU91Pzdd9gZE/tIYWrJyjU0rBfNmXMX8tOuJFynYrlIxGIxYrGYkJKBxB48TJOG9SzCcK5lzGXn5OLq+uxj/XWyYfFMDu7YhJ2DI05uHvgHh9K47SfMnfA9kVVrU7F2Q77t2IQajZoRdzAGo8HAF8N+poRvAPv+Xs/1S2f56OtnFyIz7qWSp84lKNSy8KzesBkn9+96pDC1489lVKjVgOsXzxYkCgTodVoMBj1msxmD0fDCtU+vkxPr53L54Bbkdo7YOrnj4l+ayCYfsWf2SHyjahJYqT5Lv2lOSM33uBEXi8looOEX41B5+nMxdiOp189T8+Nvn7l8dUYaurxc3EtGABBc412un4h5pDB1dvtKAirUIzXhXKF0r9DKJJ4/bnX/uV1rqPf56PwNHhv7p29UvonM23marSevo1LKcVcpKO3lRMfaZRi18iA1y3hRL8KXFuPX816FAGLP38FoNPFDx5r4uzmw8dg1Lty5R//mTzeFfxxpWXnkag2E+1rmoHfLB7D37G0rYUohk+T/r9EVaFMFgM5gRG80gRkMRjNOdvJCz95MzSI9R0tUgOsz1/N5eC3ClDbxAnkXYvD4bA5mo5HkOd2RPDCd+zdChQMe3WaTfexPsg+twKnpgMfmq7l+koztU6zSBRI57p2nFkozZqcisiv40sX2rqizU//9KMbsVMT2BfeJ7F0xZqcidQsiM3YhdlXbWs5e3DiJ2LVgq//ehvForh5G4uKHquEXj/8y3kDSb5wjKW4PdQYtwmw0EPNjZxx8Sj3yXqlSRfR3C0nYu4arO5cS1fHxL620S8c5s+Y3q3SRVEatb2YXStNkpCJXueV/tlG5ocmw7h+ApFN7OL9hOtrsdKp8PjE/3WTQEfNjF4QiESUbfkyJyOhCz5mMBm4f2Up4m2dfDL0uzqdq2JuQy9xWXhhN0G3dbUq5PNrlrYNMxJxW3qw7l8mK+EwG1H78ZHMiMY+pB+9ZpcvEAqY1L6zeTs014qosmEJclSLScoumiTOazHy27g53svS0CLUn1K1gghwXk8LhW3n4qSR8WfXtXnxk3TxPavweKvVfiNlo4Oikrth7P3pMSZQOVP5mPrf3r+XmnqWU+XDQY/NNv3ycy+t/t0oXSuVU7FU40Ks2MxWZQ8GYkqtc0WY9ekylxsdwdfMMdNnpRHb/+b808a3j9LnzbNsVw7ol8zAYjLTu9CmhpR/9HnJ0cGDNH3NZtnod8xcvZ/TQgY/N9/CxE4z/xfo9ZCOXsXRuYU+sySlpuLsVjEd3N1dSUtKsnk1OSWXHnlgWTP+NoefG56eXDg5i2pwFdO74IRqNhiPHTxIU6A/AqCED+LzPAORyGUqlguVzZzzx+3gTSbhwhuOxOxk1axUGg4GRPdvhHxz6yHtt7VWMmLGCXetXsHXVQrp8M+Kx+Z6PO8Ly6da/a6lMzpDfC1szpKel4Ojinv/ZydWd9DTrDbj0tGRO7N/FgJ/nMP/i8Pz0kqGRlI6qRN+2DcBspl6Ldnj6BeZfn/fTMIQiERVq1adZx88ea572JpJy7RwJx3bRetQSTEYDa0d0wsW/9CPvlds60HrkH5zduZpTWxcT3XXoY/NNPH+Mg0t/sUoXy+Q0Hzq3UFpuegq2TgXzmtLRDfUjNkhz01O4fmIPTQdOJ2buOavrjyIr5TZXD2/n+ok92Ng5Ur3jNzh4+P6nZ98Uzt26x+4zt/ij97sYTCY++X0Lpb0eLRQ6KGQs6tWE1QcvsWTvBYa0qfLI+wCOX03m143WAqhcImb2F40KpaVmqXF1sMn/7OagIDVL/e9HAdhz5hbT/z5Feo6GiZ0ta7YIP1cqBLrT9Id1mM3QpnoIAW4OhZ7bfuoGDcr6vrbx81qEKd2t09iE1EQgliEQgzzEegfhITalLKYt0hIh5F3Y+8R85f7l8Og+94n3vCjkQZXQJV0gZcGXCBUqZF5hCAQF5inO73+H2WQk/e/fUZ/djW1Uk1dSrxfB/WvxeJSthUgiA4kM9/DHm7KUiLL82FW+pUk6teeJ+bqEVHik2d3zUiKyDiUi63DvykkubJpF9a8nA9Bg1FpsVG7kpt3hwO9fYe8ZhNK1QF0dv+InnEtG4Vwy6oXX6WVz5q6WGv4KZGLLb6667+PjidUOsFwr5SIjNuHRE9hDynvaMLf1CzhX8R8RCQXMbe1NttbI0O3JXLuvI9DJEmNjULQbRpOZ3w7cY9fVXN4tZffK6vWiyUyIxyW8YEy5hD3eTNktog4Adt6lSI2PeWK+jsEVqNx/4YusKgCuZaNxLRtN+tU4rm2ZTbme1psgbzsnT52mXnRNZDIZMhnUrfn4PmlY12KKFFq6FNt3P/k9VKViedYtmfdC6zpu0mS++epzK3PUGlUrc/rcBTp8+gVOjioiI8IQPbhn0bKVzPh1ApHhocz9Yxk//jrliULgm8jls3GUq14HiVSGRCojqurjTcIq1LKcf/ELKcPxfTufmG+ZqMqMnLnyhdZ12bSf+KBbH6s+Sr5zk6QbCUxcvg2AiQN6cOn0CUIiyvPZ4LE4uriTp85l2sh+HNi+kRqNmr3Qer1Mki+fwq98NGKpDJDhF/X4tUJAxboAuPqX5vrx3U/M17NMRVqPXvIiq8qBJZOo/MFXRTIjNhr0iCUyWo1YRMKx3cTMG837g2c//cE3iPgbqdQO9UYmESFDRM0yjz8fXCfcYiJX2suJPWdvPTHff5vYvSjqhPtQJ9yHk9dSmLk9nind6nMrLZvrKVlsGNQCgF5zdxGXUIKogAIhenv8DUa0fbws8bJ546MhCsQWtZ9AIATTk3e9i6KZEtlZNEwPMWQV1lT98z5DVioP9/yN/7jPvubH2Ne0HAi/t240YufCtpoCoQhFaD2yDy57q4SpoiAUWxa+AoEQ81POBxVFMyVXuaLJKNhdystIQa56svrWuWQ51GmJaHMyLOaJDzRbShcvXILLk3n7Ur4wdXHzXHQ5GUR2G/f0Rr7lSESWnRqhQPDUMz9F0Uy5KkWk/uNgc2quERfls5nj2clElPO04chtdb4wBRZhq36QkmWnMt9qYaoo5M95QiHmp8x5RdFMyRxc0WYWjClNRioy+yePKcegKM7fS0SXk4HU9iWdWXsLkEotfSISCp8apLsomil3NxeSUwreQ8kpqbi5WZvjnT1/gW+GjgQgPSOTvQcOIRKJaFCnFp937cTnXS3Ofr4dOgo/Xx/up2dw8fJVIsMtWpwmDevxWe9nN7t+GxBLLPOGUCjC9JQ+KopmytHFjfS05PzP91OTcXRx+/ejXL90lhk/WITVnMx04o/EIhSJSL59k8DQCOQ2FvPZiMo1uHruFCER5fM1XjYKJVXqvUvCxdNvlTBVFEQP1wpC4VP7pyiaKaWjGzn3C+a13PQUFI7W/ZN2/Tw7p1u0YZqcDG7GH0AoFOFfoc5j66F0dMO/ouW6f4U67Jk76on1ftuRPticFQkFGJ9y0K4omilXewWpmXn5n1My1bjaP9kpW7lANxJX55CRqyHm7C3CfZ3zzQCrhXhy+mZavjB1OTEdo8lMae/XZ4b5WoQpqU8E6ZsnYl+jA2aTEc3lgyjLNX3ufIuimRLZOSOUKdHePovUKxT16b+xrWTthMAmpDo5x9ahCKuH7s45hHIlIjtnzCYjJk0OIoUDuuSr6FKu4hQ4yGITnX4HiZM3ZrMZzeX9SFzeLrWwU2BZ4pf9SHCjTphNRpLP7MevRvPnzrcomim5gwtiuZL7CWdw9A/j9pEtBER/YHVfTuotlC7eCAQCMm5dxGTQIVU6oFNnIZLIEUmkaHMyuH8tnpINLF63bhzYQMr5Q1T/evIbcdj9WQj3kDExNo2OkSqMZjh4U02zMs8vbBRFM+WsEKOQCjmbrCHUTcbfl7NpHebw9AcfkJFnRCS0CFJag4ljt/PoEOmA2WzmTpYBbwcJZrOZ/TfU+KokT8/wDcYhoCwXVk3Ar/7HmE1G0s4dwKvq+8+db1E0UzJ7F0QyJZnXz2DvF8bdY1vxrtna6j516m1sXLwQCARk37aMKYnyv/fr20K5yAhGjPuZzz7piMFoZM++A3zQ8vkXskXRTLm6uGCrVHDq9FnKhoeyfvPfdGxr/R7avr5Ai/LwPFeDOrUwGo1kZ+egUjlw8fJVLl65yrgqFlPr7Jxcrt+4hb+fDwcPHyXI/wV4nHnFBIdFsfDX0bzX/lOMRiOnDu0l+r02z51vUTRTKmdXbBRKrp6LJ7BMBAe2/0WDFtZu/Scs3pL//8PzXOVr1OPI7q3EbF6Lsb0Bs9nMxfjjNGzVEaPRgDonGzsHRwwGPacO7SW0/OPNqt5E3IMjiV04jqj3PsFsMnIzbh+l67R8+oNPoSiaKYXKBamNkuQrp3ELCufy/s2ENWhrdV/7nwuc5jw8z/UkQQrAv3w0ieePY+/qRdKFE6jeMhM/gLJ+roxfd4ROdcIwmkzsv5BIi8olnzvfomimXOxtUMrEnLmZRpiPM5tPJNC2urVJ9a20bLydbREIBFy4cx+9wYSDQoa7Ssn6o1foZLQ46TiZkMKHNQrM5LedukGjyNc7v70WYUrmWRqb4OrcnfUpQqUjErdAhDLbV14Px8Z9uPfXeMx6HTYlKyMPskxkOcctg862QnPkJauiuXKYpKkdEUpkODV7YCZhMpCyqBcAQpkC5+ZDEAjFmM0mi7t1bS5mzEjdSuL47tt1JsfRLxT3iFrsGfsxMnsn7D2DkNi8+v4p2/ZbTi4eg1GvxS20Km6hloPy12MtHnf8a7UiKW4Ptw9vQSASI5LIqNB1jOXg9d3rnFr244PdfRMlG36MXYkAAOKXT8DGyYPYiRbPhCWioinV5Plc575qyrjKqeGnoOuaOzjaiAh0kuQ7bXiV9K3hwviYFLQGi2v0Kj4Wu+j15yxu+5uH2nNPbaDHn3fI1ZkQCgSsPpPJwjY+3FMbGBuTislscTRRJ9CW6n5KTGYz42JSyH0QxCbISUa/mm/n4fmH2PuWwSWsJkd+/gSprSO2HoGI5K9+TJVq8w3nl/2AUa/FuXTVfOcTdw6sA8CrektS4/dw95hlTAklMsI7jcq3Qz8+uSfqlJsYtWr2j2xB6Q8H4Vz67VoAPiQitAx1a9WgRYcuODs5EVIyEDvbV98n3w/ox+BR49BqtdSqXiXf+cTyNZb3ULvWj9/IMhgMfNTjKwBslUp+HDUU8QOvoqMGf0vv74YiFAixt7djzPffveSWvHgCSocTVa0Ow7q3wd7RGe+AYGyUr76PPuo1hHk/fY9OqyWicg0iHjif2P2XRSCr28x68f6QirUbcj7uCMO6twEERFSqTlS1Omjz1Ez6ridGgwGTyUho+apEv2u9ufEm4xYYil9ULdZ83wEbByccvUsifQ1rhZqdBrBnzigMD1yj+zxwPnFu1xoAQus9+XvdMLY7GUk30GvyWNK3KbW7DsEnohpR733CrpnDOL1tGRKZDbW7DHnpbXnRhPo4U6uMFx/9thknWzlB7iqU8le/Oflti0qMXnUIrd5ItVIlqPbA+cTaQ5cBaFU1mN1nbrHlRAJikQCZRMToDjUQCATUi/Dh+NVkOv66GYEAqoaUoNY/PAzuPH2DSZ3rvPI2/ROBp6fnE3V5SqWSk/FnaL7K2vTneTDp1AilCkx6DSmLeuH0bv/8+E3FPJnVLVXUrFqJ9PR0q2sNGzakz8hJLLn0fGUYtGrEMgUGnYb9v/QkssN3qB7jhKIYqOEjI/Ponwz+rsBBysmjBxFsGoExM+mllKnWm1BIhGgMJnr9lUj/Wq6EPMYJRTGPR1KvF2OX7GD58uVPvO/w8ThmnheRpn42F7YPx5RRp+HElC8o3XYgdo9xQvG/TmUvGY639/Flj25W1+Li4nCSC55q1v0iyFWrUSoU5Gk0dPrsa0YO7k9o6f+fffKfEYrRy1Ucv/tqgipr8tTIbRRoNXmM79eVzn2H4Rdc5pWU/b+At72IxGwjB25pX0r+eo0aiVyBQathw7jPqN158GOdUBTzaDqUFmN7dg2G1ISXkr9aq0chk6DRGfh85g6+a1X5sU4oink88lK1mbXtFBN+so6x+NrOTKVvmog+7Tpmgw5l2cbFgtQbxqml48m+ex2TQYtP5XeLBak3kJ9jU7mRrkdnNPNOiG2xIPWGc3HlBHKTEzAZdHhUbPL/VpB6kxgx9meuJFxHp9PR/L3GxYLUG8jCSaNIvHkNvU5LjYbvFwtSbxh7F4wl404CBr2OkJrvFQtSbyDj1x4hISUTncHEu+UDigWpl8BrE6acW37/uoou5j9Qocv/9kHL/wWG1XN/+k3FvDGEfTzidVehmH/x05hhr7sKxTyFHkPGP/2mYl4b9T8f87qrUMxTGNX+8Z5Ki3kxvJ2n74sppphiiimmmGKKKaaYYl4zb7xr9CeRsqg3qgY9kXq+OrVyxu45qOP/xqTJxnvg1vx0zY1TZGyfgj75Ks6thqEoU6fgmZ0zyLt8CMwm5IEVUTX6GrNBy701IzCk3wGBCJuQaqjq9Xhl7Xgd7P/1C8Jafo3K79WYaRh0Go7NHYI67TYCgQj3iJqENrcEUDbqdZz8YxQZNy8gVTpQsesYFM4lAMi8c4X4ZT9i0OSCQEDtAfMs8YH+H9F7YyI9qzhT2vXVtFtjMDF8RzKJWQaEAqjup6BHZUug3uQcA2P3pJCjM2EyQ49KTlT1fbJb1f8PnJj6FSXf/xJ7n1dv9hQ/dwB59xKpMmAxAGcWfY865SYAhrwcxDa2LyX+1ZvIJ5/34tteXxAe+mreQ3kaDX0HDePW7USEQiF1a1Wn31efA5B4N5nBI8eSlZ2DyWSk75c9iK5RLf/ZxLvJNPuwE19270zXj9qTlJzMoBFjSbt/HwEC2rZsxsftrL2mvo382O9T2vboR0CpsFdW5pp5kzmw/S/U2VlM33goP/3v1YvYu3kdIpEIO5UjXfqPxMXdcgB//7YN/LXEEhqkWcfu1Ghk8fJp0OtZPHkcF08dRSAU0qrL11Ss3YBl037iwqmjAOg0eWRlpDN1/b5X1saXxV/jPqdqu164Bjw6KPOLxqDVsH3qILJSbiMUCvGNqkWVthZnLhdjN3J45e8oH4RjCWvwAaWjW5B24xL7Fo1Hn5eLQCiiXLMuBFVpCEDM3NGkXj8PZnDw8KVOt2FI5P+b76meM3fQ671ylPF2fmVlTv/7FFtOJJCdp2P3qAKHLxuPXWPKlpO42lucX7WpFkLzB94K72bkMnbNYZIz1AgEMKlzHTydXo6DlLdamHod2ARXw65iS5KmdSyULnZww6nZd2QfWlEoXXvrDNpbZ/D4zOKyPWXh12hvxCH1KoNd1Q+R+5fDbNSTsrgfeVcOY1Py7fSM9aZSsn4HXEIqYDLoOTD5a5LPHsQ9rBo3D/6FxMaOBiNWc+fYds6tn0rFrmMwGQ2cWDiC8p2G4+AdjC4nE6GoeJi8Cj4sq6K8pw16o5m+m5I4dEtNVR8Fi06mUzfQlhah9lxP1zFw611W+L59Lmr/V0iJ34NIWniREN5pdP7/l9dPRix/fBDpYp6fLh3bUaVieXR6PV2/6MveA4eoXb0qM+ctonH9urRr04Ir167zed8BRK8vEKYm/DqFWtUK3jFikYgBvb8gtHQpcnPVtOnUjWqVK1Ey0P81tOrtJ6pqNPWbt2PQJ4Vd7PuWLM2waUuRyW3YvWElq2b9Qs/vfyInK5P1i2YwbNoyBAIBI3u2I6paHZR29mxcOht7lRPjFv6FyWQiNzsTgPZffJuf7451S7l55cIrbeP/EpFNOuJZpiJGg55NE77gZvwBfB94Agys3JCaH39b6H6xTEbd7iNw8PAlNz2VtSM64R1eFZnSjmod+uZ7Mjy47BfO7lhFVNNPXnmb/lepVcaLD6qF8MHPf1lda1DWl/7NK1mlj1xxkM71wqgSXAK1Vo/wgVfal8ELXSWadHncWzsSY1YqZrMRh5qdUITVI3PvQvIuH8Bs0CLzDsfx3W8QCASkLOqNxCMY7a14zDoNTs0Hk71/CfqUayhC6+JQtxuGjCRSlw1A6lEK3d1LSFz9cWo+GKFEXqhszdWjZO6dj9moR+zoiVOzgQilCjJ2zSTv0gEEQpFFK9Tgi+dqo8z70btcYpVFq8G/O0sgwGzQYTYaADNmkwGRrRNCiRy5fznLLSIJUo8QjFmpvEoM2jyOzRuKJiMFs8lESOMueFVowMUtc0k+vR+jXotTQARl2w9EIBCw/9cvcPAJ4d6VUxh1eZTrNIzL2xaRnXgVz/INKNOsB+p7SRya1gcHn9Jk3rqIXYlAynUahlhauL9Szh/m4qY5mAw6FK5elPtoKGKZgnPrp3H3dCwCoQi30pUJa9XrmdsnlspxCakAgFAswcGnVH4g4LvxsZR61+IOvUS5upxeNRGz2UzqhSPYe5XEwTsYAKntmxFfJ09vYsTOZFJzjZjMZjqVc6RekC0LTqRz4IYandFEmLuc/jVdEAgE9N6YSLCzlPi7GvIMZgbXcWVJXAbX7uuoF2hLt0pOJGXrGbDlLiEuMi7d0xLgKGVwHVfk4sLWv0dvq5l3PB290YynvYTvol1RSITMPHKP/TfUiIQCKnnZ8EXVZ9+lkouFlPe07CxJRAJCXKT5AYEFgPqBm/QcnQlnxbMFBn7ZGLV5nFn0PdoMy/zn37Az7uUakPD3PNLO7cek1+LgH0GpDwYgEAg4MfUr7LyCybh2CqNOQ2iH77mxcxE5Sddwi6pP0LufkXc/iVOz+mHnXYrs25dQegQQ2uF7RP8aT/cuHiZh61xMBj02Ll6UaTcYsUzBlY3TSTu7D4FQhFOpygS//9VztdGgVXMrZgWlPxjAmUXW517NZjMpp3ZRrqd1IOFXhTovj36Dh5OcnIrRZKLnp51o0rA+0+YsYE/sATRaLeXKhjNiUH8EAgGffN6LMiHBHI+LJ0+Tx7gRQ5i9YDGXrl6jSYN69O7ZnTuJSXzW+1tCS4dw/uIlSgYGMG7EEGzkhfth/6EjTJk1H51eh4+XFz8M+w6lQsGkKTPYFbsfsUhE9SqVGND7y2dun41cTpWK5QGQSiSElg4uCPwrgJzcXABycnJwcykYkzv2xOLlWaJQnV1dXHB1sYQdUCoVBAb4kZKa+lKEKW2emumjB3A/LRmzyUizjp9RuW5jNvwxg7iDe9HrNASFRvFJ3+8RCAT82O9TfEuW5tKZE+g0eXQbMIZNy+dyO+EKlaPfoVXXr0i7e4dJg77APziUG1fO4+kXRLeBY5DJbQqVfea5qIlJAAAgAElEQVTYAdYvnI5er8PN04eu345CbqNg1exfiTsYg0gkIqxiNT7s8c1ztTEotOwj08tEVc7/P7BMBAd3bsqvV1iFqtjaW94zYRWqcvrofqrWa0Ls1j8ZO+9PAIRCIXYOjlb5Ht69lRaf9HyuOj8OvTaPHVMHk5uejNlkovz7nxJUpSHH18/hZlwsBp0W95JlqdV5EAKBgL/GfY6zXwh3L8Vh0Gqo030EcZsWcP/2VYIqN6BS655kpyayeWJvXP1Lk3bjIo5egdTtPgKxrPA4un3mEMfWzcJo0GPv6pWv5Tm8cgo34mIRCkV4h1eharvez9w+sUyOZ5mKAIjEElz8SpP7j+C/j0LlURDLSOnoio29I5rsdGRKu3xBymw2Y9BpLS+uV0SezsCQJftIyVRjMpvpUi+chpF+zN1xmn0X7qDVG4nwc+G7lpURCAT0nLmDUp6OxF1PJU9nYHjbaizcc5ardzNoUNaPz9+JJPF+Dn3n76GUlyMX76QT6O7A8LbVkEsLiw2HLyUxe0c8OoMJb2dbhrapikImYeqWOGLP30YkFFIl2INe75V/rjaG+xYtPEpCciZGk5kqwZa1+cOAvy+LFypMaa4eQWTrjGs7y4FRkyYHANtKLXGobZHQ7/35A5rLB7EJsUj/ApEEj09nkX1kNWkrh+Dx6SyENnYkTe2AbRWLuYHh3i2cmg5A5hPB/b9+JOfYn9hXa5dfrlGdQda+P3DtOBGh1IasA0vJPrQK24otyLuwD4+eixAIBJg02dZ1vn6SjO3W0eoFEjnunac+93ci8w5D7h9F4q+WQIy2FVsicSkcXMykySbv8gHsKr/aGBMp5w4hd3Chas+JAOjzLP0VULtNftylEwtHknxmHx4RtQAQiiRED5zPtd0rODJzINED5yNR2LNzZBuC6lr6JCf5JpEdBuMcFMnJxWO4vncNJRsUaPK0ORlc2rqAal//jlhmw+Xtf3B11zICarUh6VQM9b5fjkAgQK+27q+0S8c5s+Y3q3SRVEatb2Y/tq16dTbJp/cRWMeiHtZkpmLj6P6gTWLENrbocjPJSbmJAAEHp/RBl5OOZ4WGBDf8qMjf7YvmyG01zgoxPza2TAw5D4SLVqH2dC5vecmO2Z3CgZtqavhZtAJioYBZLb1ZfSaTIduSmd3SCzuZiA4rbvJBhOXlfTNTz4DarkR4yBkfk8Kf57JoV1aVX26GxsiikxlMercENhIhS+MyWHk6k5ah9sReV/PHB5aAydlaazfWJxLzmHrQOqSCTCxgWnOvx7Y1W2vkwE01bcItdexSwZFvNiex9lwmeXozk94t8Sxf4Uvn3oVDSO1diOz+M2AxdwPwrtmGgHe6AnB2ySjunduPS5glTo1AJKFSv3nc2ruS+HkDqdRvHhKFPQd/aItv9IcAqFNuUvrDQagCynJ++Vju7F+Lb90O+eXqcjK4vn0h5T7/DZHMhhs7F3Nrz3K8a7Ym7XQMVb6z7Hrr86zHU/rl41xeby34CKVyKvaaaZWesGU2vtHtEP5LmHtIxrVTSG0dUbj6FOWre6HsO3gYNxcXZvwyAYDsHEs/dPigFV906wzAwOFj2LPvAHVrWQ5mSyRiVi2azR/LV/FV/8GsXjQbB3t73mnVnk/aW+aMhBs3GT10IOUjIxgyejzLVq+j60cFwVvTMzKYMW8Rc6dOQmFjw5yFS1i4dCUd2rRkx55YNq1ajEAgICvbuh8OHzvB+F+s30M2chlL505/bFuzsrPZE3sg3zTvq+5d6Pb1NyxZtZa8vDzmTvkFsLiAn7toKXOmTGT+4keHALiTmMT5i5cpG/ZyzKxOHz2AytmVPmMt7VTnWL6Hes3b8/7HFjPF2eMHc+pQDFHV6gAglogZPm0Z29cu4ffhfRg+bRlKOwe+6/QejVpb5uW7t67T5ZsRBIeXY95Pw9i9YSWN2xZoBLIz09m4ZDb9J8xEZqNg8/J5bFu9iHrvt+PE/l2Mnb8egUCAOifLqs7n446wfPrPVulSmZwhv/+3APT/JnbrOiIqWX53GWkpOLl65F9zdHUnIy0lvy7rFkzlwqljuHn60PHrQTg4FgjHacmJpN29U0hQe5Hcij+IUuVCk36W35BObRlHYfU/oEJzS0iDXTOHczNuH37lLOsDkVhCqxGLOL1tOdt+70+rEYuQKe1ZPqAVEe9Yxkrm3RtEfzoUj+BI9swdzdldq4lsUvCO1WRncGLDPN4bMBWJzIa4TQuJ/3spYfXbcP3EHtqOW4VAIECbaz2OEs8f4+DSX6zSxTI5zYfOfWxbtbnZ3IiLJbxhwboy4dgu7l48iYOHL9Xa98XWubDDp5RrZzEZDNi7FcQ72jNnFLfiD6DyDKBauz5P/oJfIAcvJuJib8OkLnUAyNHoAGhTPYRPG0QAMGLFAfadv5Mfn0ksFrLg68as2HeBAYv2suDrxtgrpLSesIH2NS0myzdSsxjcugqR/q6MWXWINYcu07F2gVl5Rq6G+bvOMLlbfWykYhbtOcey2Au0qRZCzNlbrPimqWV9kKezqvPxq8n8uvG4VbpcImb2F42K1P7dZ25xMiEVXxc7+jQtj7tKyc20LOxsJAz8Yy9J93OpFOzBF40jEQlfjquIFypMSdwCydgxjYydM7EJrobM17JLo71+kuyDyzHrNZg02Uhc/fOFqYd/Ja6BSFz9EdlZJguxyhNjVgpCuS0iezdkPpYfhCK8ITlH18A/hCndnXPo066TstCy62o2GpB5hSGUKxGIpaRvnIA8uBo2wQXmDg+R+5fDo/vjB9nzor9/G33aTTx7rwIgdUl/tDfj878bs8nAvXWjsavUCrGj50urx6Ow9wzi7LrfOffnVNzDa+BcMgqAtEsnuLJjMUadBr06C7sSAfnClHuEZRFo5xmEXYkA5A6W3QKFsxd5GclIbOywcXTHOSgSAO9KjUmIWQUUCFPpCWfIuZvAvkmWM2Imox6ngHDENkpEEilxS37APbwmHuHWHmhcQipQZ1DRXmImo4HjC4YRUOcDlC6PX8QDmI1G7l87Ra1v5yGSyjn4+9eofEvhWspahfwqCXSUMu3QfWYcvkc1XwWRJSw7ryeT8lh2KhONwUS21kSAo5QaD2T1h0JVoKMUf0cpzgrLcC9hJyEl14CtVIibUkSEh2Vh3KikHWvOZtLuH5ur55I13EjX8dWGRAD0JjNhbnKUUiFSkYAf96ZS3VdJtUecYSrvacPc1t5W6U/CYDIzalcKrcMc8LS37CTtuJJDkxA7Piyr4kyyhh/2pLCgjfdLVdk/C7YlgriyYQpX/pqGS1h1VIGW8ZR+5Tg3dy/NH09Kj4B8Ycol3PJXWSIIpUcAMnvLeJI7e6LJSEZsY4dM5Y4qwNIp7hXe4XbsqkLCVNaNs6iTr3N8smVBajIacPALRyRXIhTLuLBiHM6h1XEJtR5PjsEV/vPZpuw7l8i7d4fgFr3Ju//o2GkpJ7fjXr7hf8rvZRESFMSE36YxcfJ0omtWp2I5y1x05PgJ5v6xDI1GQ2ZWNiUD/fOFqbq1Lf0QHBRIycCAfG2Nt2cJklJSsLe1xcPdjfKRlvdQs8YNWbxiTSFh6tTpc1xNuEHHbhatk96gJyo8DFtbJTKplKFjfqROzWpE16xuVecqFcuzbsm8IrXTYDDQf+goPvqwNT5elnfHpr930qJpE7p0bEdc/BkGjhjDhmULmTp7Pp3af4BS8egzHLlqNb2/+55B/b7G1vblmGh6B5ZkxcyJrJr9C5FVowmJsOxUX4g7ypaV89FpNORmZ+LpF5QvTD386x1QEi+/IFTOljMsriW8uZ96F4WtHU6uHgSHW6w8qjV4jx3rlhUSpq6ejyfxxjXG9ukMWM4iBYWWxcbWFolUxvyfhxNZtTaRVaOt6lwmqjIjZ658Yd/BwR0buX7xHAMnPbmvjUYj6anJlAyLol3Pb/l79SJWzpxI9+/G5t9zZPdWKtZqgFD0cjT1Tj5BHFr+G4dXTsY3siYlSlm+48Tzxzm15Q8MOg3anCycvALzhSm/crUtz3oH4egViEJlGUd2rp7k3EtBprBF6eSOR7BlTAZXa8yZ7SsKCVPJV0+TnpjAhjHdHnwXBtyDwpHa2CKSSImZNwa/yJr4RtW0qrNnmYq0Hr2kSO00GQ3smjGU8AYfYu/m9aAdNSlZtREiiZRzu9eyZ84Img4s2NRQZ6Sxe9Zw6nQbjuAfi/M63YZhMhk5sPhnrh7ZTqlazazKexmU9FDx+6aTTNlykpqlvYgKcAMsAsvivefR6gxk5ekIdHfIF6ZqlbH8DfJQEeDugMuDM0deTrYkZ6qxlUtwd1AQ6W8Zc43L+bPywKVCwtSZm/dISMnis+nbANAbTUT4uqCUS5CKRfyw+jA1ynhRs7T12rZCkDt/9H73udteq4wXjaL8kIpFrDt8mVErDzH1s/oYTWbiElJZ1KsJ7ioFQ5fuZ9PxBN6vFPTcZT6KFytMOfvg3m02miuHyNwzF5l/eeyrtyN966+4d52J2MGNzJj5mA0FUqpA9ED1JhAU/P/gc0HQRmvTuUKYQR5QEedW1m5u3btOR3P9BHnnY8g5ug63jwvvWrxszVTexX1IvUIRPjhjIA+qgvb22XxhKn3TRMRO3thVefWHfm3dfYkeuIDkswe5sHEmLqUqUrLBR8Sv/InoAfOxcXTnwqY5GPUF/SUSSwEQCISIxAX9JRAIMBsf019WmHEtXfmR7tdr9Z9L2qVjJJ7cxfW9q6neq3DfPItm6tSy8ShdffI1ZwByB1fy0pOxcXTDZDRgyMtBqnTARuWGU1AUMluLdsYtrBqZty6+dmHKRyVldksvDt1SM/dYOuW9NLQv68Av++8xq4UXbrZi5h+/j85YEFBWIrL0g0AAUmFBnwgF8PA2wVMEEjNQwduG4Y9wwz6jhRcn7uSxJyGHtWcz+bVp4QnzWTRTP8em4u0gydecAWy+mM1PTSzaqHB3OTqjmUyNCUebN8vcT+HmS6V+87l3/iDXNs/GMbgCvvU6cnHNRCr1nYvc0Z1rW+di+sd4Ej4YQwKBAOGDsWX5LMT8cP6z6qJ/J5hxDKlE+McjrepUse9s7l86Rmr8Hm7vW0P5LyYXul4UzVTm9bNk3brAgdGtMZuM6HLSOTH1K8p/aRmjJqOBlPgYKvUrmlDwovH382H1ojnsPXCI32fMoWqlCnz6cXtGT/iFlQtnUcLdnSmz5qHVFvSDVGLpB6FQmP//w89Gg6Uf/j1W/v3ZjJnqVSry85jhVnVasWAmB48eZ9uuGJauXMv86YXnsGfRTA0f9zN+Pt50al9wGHvNhk3M+t0SUDKqbDg6rY70jEziz5xn264YJk6ZQXZ2DgKhAJlUSse2rdEbDPQZ+D1N32lIw7rWAsWLwsPbn+HTl3P6SCxr508htFxlmnzYhcW//8CwactwcvPgz4XT0esK+kUsKXjfiCX/fN8IMRof3S/Ww8NMaIWqfD7kR6s6fT9lCedPHubY3u3sXL+cAT/PKXT9RWqmzh4/xMalcxg4cS4SqaVdKhc3Lj5wJgGQnppMqchK2NqrkMrllK9ZH4BKtRsRu2VdofyO7N7KR70GF6kORUHl4UerkYu4FX+AY2tn4BlaicgmH7P/jwm0HL4QW2d3jq2bhUFfEAxYlD+f/Xt9IMRsemC2/ZRxhBm8w6pQv6e1y/WWwxZw59xRrh3bxdmdKwsJOPBsmqm9C8Zh7+6TrzkDkNsWWGeUjm7O4ZUF86YuL4ctv/SlUuueuJeMsMpPKBQRVKUhpzb/8cqEKV9Xexb2asyBi4nM3BZPxSB3PooO5af1x1jw1Tu4q5TM3h6PVl9gQSJ9YM4vEAjy/3/42WgyPfi/cDnWQ8tM5WAPRj/C9fq8r97h2JW77Dp9i9UHLjH1s/qFrr8ozZSDssBJ1vuVgpiyOQ4ANwcFIZ6OeDlbzC+jw7w5czMN3gZhypidhtDGDmVEI4RyW3JPbsoXnIQKB0w6NeoLMShKF23CNmYlWwQQ7zDUZ3cg9Sn8A5Z6hZK+9Vf0928jcfLGpMvDmJ2GyM4Zs16LTcmqyLzDSZrawSrvl62ZEtu7kXNyI+YaHcAM2punsKvcBoDM3XMwaXNxbvrtU3J5OWgyUpEo7fGp3BiJjS03D/6Vv9CTKh0waNUkxe2mRFTdIuWbl36X+9dO4xQYwZ1j23B6oKV6iKN/OKdXTiQn9Ra2rj4YtHloMlKRq1ww6jS4h1XHKbAsO4Zbmz0WVTN1/q+ZGPJyiepQ+KXjEVGTW4c34xQYQdLJ3biEVEAgEOAaWoUrOxZj0GkQisTcu3KykBD2ukjLNWAnE9Io2A5bqZBNF7PRGc0AOMiFqPUmYhJyiQ4o2q5yco6BM8kawt3l7LiaQ1mPwuZbYW5yft2fxu1MPd4OEvL0JtJyDTgrxWgNZqr6Kgj3kNN++U2rvIuqmZpz9D65OhMDarsWSnezFXM8MY8mIXZcT9ehM5pRyd+8qA7azFTECns8Kr6D2MaWxMN/YXow/0mUKgxaNanxu3EtW7TxpE1PJvP6GRz8w0k+sQ1VYOFzGfZ+YVxcMwl16m0Urt4YtXloM1OROrhg0mlxCa2OKqAsB36w3rApimbKu0ZLvGu0BCDvfhLxc77NF6QA0i8dQ+nmh1zlVqT2vWhSUtNwsLfj/SaNsLe1ZfX6jWgfLNAdHVTkqtVs2xVDo3pFew8l3U0mLv4MUWXD2fT3DspHFX4PRYaHMXrCL9y4dRs/H2/UeXmkpKTh6uqMRqMlukY1ykdG0KiF9XxSVM3Ub9Nnk5OTw+ghAwqll/Bw59DRE7Rs2oSrCdfR6nQ4OapYPLugn6bMmodCYUPHtq0xm818P/pHAgP86NzxwyJ9H0UlPS0FW3sHqjVoio2tHbGb16HXWRbitg4qNHlqjsdup0Ktomk276UkceXcKUqGRnJo15Z8LdVDAsuUZfHv40i+cxN3L1+0eWrS76WgcnZDp8mjbJValAyLYuDH71nl/aI0Uzcun2fRr6PpN24a9v8w1QuvWJ21834nN9ti1nf2+EFaf9obgUBAVNVoLp46SplyVTh38jCefgWLwKSbCeTmZBMUGmlV1osiNz0VmdKe4OpNkCpsuRCzPn9jVW7ngF6jJuHYLgIq1itSvjn37pJ8JR73kmW5cuhvPIKjCl13Cwpn3x8TyEy+hYO7D3ptHrnpKShVrhh0Gnwja+ARHMmyb1tY5V1UzdTRNdPRqXOI7jKkULo6Iy1fq3bj5F4cSwQAYDTo2fb7AEKqv0tgpQLhwGw2k5VyGwd3H8xmMzdOxqIq4f+f6/G8pGapsbeR0aRcAHZyKRuOXkX3YBPIQSlDrdWz+8wt6oYXzfz6boaa0zdSifBz5e+4G5T1L/xuDvd14ef1x7iVlo2Pix15OgOpmWpc7G3Q6I1UL+1FWX9XWk3YYJX3i9JMpWXl5WvVYs/dwd/NHoAy3k5k5+lIz9HgaCvn2NVkyni/vGDFL1SY0qVcI3PnDIs4KxTj1KQvQrkdtlHvcXdmF0S2TkhLFN19rNjZh5xj67j/149IXP2xrdC80HWRUoVTs++4t240GPUAONT5FKFUQdqqIRaBzmx+bucTYHFzrj6zA7NeS+JvbVBGvYdDdBe0iRe4t2ooJk0OmssHyYxZQInPF2BTJhrN9ZPcndkVBALkQZWxCamOISuFrP2LETv7kjynO2A5T2Vbrulz1/G/kpV4lbN/TkEgECIUiSn74bdIFHb4VW/O7rEfIbN3QuVbdLfLtu6+JMSuIW7JD9h5BOBfq1Wh6zI7R6I+GsqJ+cMxPlhslmnaA7FcwZFZAywTttn8XM4nAPLSU7j89wJs3f2I+bEzAAHRbfCr/j6+1ZtxYtFIdoxog1RpT4UuFm9kUoU9gfXaEzvB0l9uYdVwf4S54avm2n0d04/cRwiIhAL61XTBTiaiaSk7Oq++jZNCRKlncGPu6yDhz3NZ/Lg3FX+VhOahhZ1IqGxEDIp2Y9SuFPQmi/DWraIjCqmQwduS0RnNmM1mvnwO5xMAKTkG/ojLwFclofvaOwC0DLOnaWl7vqzqzE+xqaw6nYkAGBTt+lSN2usgJ+kaV/6aikAgQCASU6pNfyQ2dnhWfZ/DP32EzM4Zu2dwY65w8+X2vjWcXz4Wpbs/XtVbFroutXWkTPshnF08HJPBMv8FNumOSK4gfu53FoHObCa4+dcvpJ2PIzluB+7lG7zUMv4Ll65c4+fJ0xAIhEjEYoYN7Ie9nR1tmjeleftPcHF2eiY35gF+vixdvY6hY8YTFOBPu9aFF3NOjirGDhvEt0NHoXuw6Oz1eTeUShu+6j8YrU6H2WxmYJ9ndz4BcDc5hZnz/yDQ35fWH1tMoTp+0Io2LZoyoPeXDB87gUVLV4JAwNhhg544Vk6cOs2GLX8TUjKQlh0t5/r6fNG9kDv1F8WdhMusnPULAqEQkUjMx72HoLC1p/a7rfm+W2scnFzwfwY35h4+/uxav5z5Pw/H0zeQus3aFrpur3Li0wGjmPnDdxge9EvLLl8ht1EyeVhv9A/6pV3P/s/dxpWzfuHwrs3otBq+adeQWk1a0eKTnqyc9QvaPDXTRls2Tp3dPOg1+nds7R1o1vEzRn9p2eht9lGPfGcUbbr3Yc74ISyb9hN2Kke69i+w5Di8eyuV67zzUufB+7evcHjF5AdaczE1Ow1EprSjdHRzVg1pj8LB+ZncmDt4+HF252pi5o5B5RlAaL3Cm6Y29o7U6TaMXTOGYtRb5rOKrT9HKlfy92/9Mep1mDFTrf3znUnKuZ/Myb/moyrhz9rhHwMFLtDPbF/BjZN7EYhEyJQO1OlmsXq6dmQHSZdOos3J5NK+jQBEdxuOs09J9sweiU6TC2Yzzj7B1Pxk4HPVryhcvZvB5M1xCAUgFgkZ0KISdjZSmlcKouMvm3Gykz+TG3M/V3tWH7zMmNWHCXBzoHXV4ELXHW3lfP9BVYYt34/OYNFm9WhUFoVMwoBFMWgNJjCb6f2czicAJm8+yba462j0BpqNXcf7lYLo3rAsKw9cJPbcHURCAfYKKd9/UBUAkVDI1++V46s5u8BsppSXE81fklYKQODp6Wl+0g1KpZKT8WdovsraXOdVYMhIInXFIEr0WPBayn8TWd1SRc2qlUhPT7e61rBhQ/qMnMSSS6+hYoD6XhKHZ/Sn7pCi2S2/7dTwkZF59E8Gf1ewU3zy6EEEm0ZgzHz0+ZLXTVK2nkF/32VBm9fnLOBNQFKvF2OX7GD58kcfzH/I4eNxzDwvIk1teuJ9L5KHGqCH8Zz+V6jsJcPx9j6+7NHN6lpcXBxO8n+aeb9+7iQm0bPfd2xY/v8jdtYTEYrRy1Ucv6t/3TUh7e4dfhv6NaPnrH3dVXlj8bYXkZht5MAt7dNvfslkpyay9dd+fPDDk+fa/290KC3G9uwaDKkJr7sq+STez6H/whiW9rXW2P5/RV6qNrO2nWLCTz9ZXXvzbGWKKaaYYooppphiiimmmGLeAt54YUqsKlGslXqLUDiX+H+nlXpbKWEn+X+vlXrTsXEq8T+nlXob8fIsUayVegNx8fAq1kq9Rdi5ehZrpd4SPJ1si7VSReCNF6aKKaaYYooppphiiimmmGLeRIqFqWKKKaaYYooppphiiimmmGfghXrzexy3f2yM98Ctr6KofAwZSdyd8QliZ5981+eJkz+0xHsSCkEowuPTWQDo7l4mfcski9c/oQjHxn2ReT3Z61bq0m/R3jmHzCcC13bj89Ozj64l58hqDOmJePb7E5HCEq8g9/R2sg8uA7MZgUyBY5O+SN1L5j9nNhlJntsDkZ1Lfn731o1Bc/Uwju99g6JMnRf59TyRTf3q8d6kXa+sPLA4rtg1ph22bn6FXJ+bTUZiJnTBxsGVKj0nArDvl88xaNQAaLPTcfQPpfJn1jFE/snBqX1Iv34W58Cy+fkAnPxjNPeunEQst8QiKPfxUBy8Q9Cps4hb/AO5aXcQSaREdRyCvWcQRp2G2ImfkX03gUY//JUfj+pNofH8BLZ2CXilZSZl6+m06ja+DhLmtvYmJcfAD3tSSM8zIgCalbGnTXhB3Kg1ZzL581wWQgFU9VXQs8rjvQw9Ka/ph++x7XIOH5Z1oF3ZN6sfnkTMdw2IHr/jlZaZdz+Jw+M7oHDzzXeFfmvvShIPbQCzGc+q7+MT/WTX2KlnYrm2ZbbFW6FQRHCL3qgCLa6Zr/w1lXvnDmA2m3EKqURwyz4IBAJOTP2K7FsXKPflZOyfwZPhs1Ih+h2Ox/z9ysoDi5OKph9+jL+vb76b8yGjxxOz7wBOjo6FzASnzJrH6vUbcVRZfrcPPehlZGTSZ9AwTp+7QMumjRn6bd/8Z86ev8jgUWPRaHXUrl6Vwd/0eqI3t2vXbzBk1HjOXbxE757d8gMMa7VaOvX4Gp1Oj8FopFH9Onz9mcWT39DR4zl7/iJmzPj7+vDDsEEoFQoWLFnB6g0bEYtERERE8P2osY8t97/Qs2lVpm889Fx5FJW0u3cY0rUlHj7+hdydm4xGRn3RHpWLG31+sLiQP3/yMCtmTsJo0OMXHEqX/iMQicSoc7KZPX4w91LuYjIaeOeDT6jV2OLV8dNG5fAOsHg7e+it70ksm/YTFx7EmNJp8sjKSGfq+n0A7N+2gb+WWOImNuvYnRqN3gfg0K4tbFo6B4FAgMrZle6DxmLn4MjKmZOIOxSDWCzB1dObT78dhcLWnkunT3Ah5Qqh9Z8vjuW8HtF0nRnzXHkUlezURFYO/hCVhy+tRy8hI+kGO6cVhDjJSk2kYsvPCsWHit+yhEMrfqPT5G3I7QreCSnXzrF+zKfU7zmmkEvzR3Fk9TQuH9iMNje7UJsPLJ1E0nlLXCSDTkNeVjqdp++yim2VkXSD+j3H4F+hDnfOHeXQit8xGfS4+JcmuutQhCIxVw9v5+ia6ahK+NG4r3VcrBdJ3SI7/GcAACAASURBVGEr2T2q7dNvfIEk3s+h/aRN+Lra5bs/H7PqEPsv3MHRVl7IfHBn/E3m7DjN9dRM5n35Tr7XwX/mARY37ANbVv5P5S/Ze57Jm0+y9ftWqJRyrqdkMmb1IS7eSefzdyLzAw9r9Aa6T9tGQkoWGwe3QKWUPyXnx/NKhKnXhcjR0yqGlOvHv+QLOA/J2DkT+1qdsSlZhbwrh8jcOQO3TtaBYf+JXbV22Oq15Jwo7D9f5hOBzf+xd56BURX9Gv9tSbLpvffQIaH3XkIRRUSKIAoooCJiAURBQEDpIFWRDiJFAZHeIXQICQkl1DRCet0ku5vtez8cSIgbIAH09b73Pp9250zbc3bmzL8+NVqRval82k6pkzce7y5BbG1PSfwlCvYvxPP9MsI5ReROLNwCMWqUpWWufSaTt2d2lX7z/2bYuvmZcUglnvwde88g9Oqy+9L2i59LP19ePRGv+u2e2Xf18MEYtGrun/3T7FrdNz7Bp1F5rox7hzfi6FeT5h/MpTgzmeu/L6D1p8uRWMroOPEXjk7tY9bP/2X4OkhLOaUkYhjd0pWablaotEZG7kqjqa81Qc6WXEkv4dx9FWv7+mEpEVFQ8vSMbU/ra1QLV2TSf1+K9H8rrN18SwUpRUYi6Rf30PTzNYgkUq6uGodr3TbYuD+ZF8y5RhOa12uLSCRCkR7PjV+m0PLrrRQmXacw6TrNvxTWbvSyUcgTYnCu3pjGo5dz5cdP/pHf92+Av69vOb6oPq/2YHD/Pnw9zVz4GDKof6mA8wiWVpaM+XA49xKSiE9MLHdtxtyFzJg0gfqhdfnw8wmcuXCJ9q1bPnEujg4OTBr/KccjzpYfw9KSdT8txtbGBp1ezzsjR9O+VQsahNXj6y/GYGcncNXNXbScLdv/YOTQd6hTqwbbN67GWibj0PHTD3m7/vc5tnj4+JnxRh3dtRnvgBBKVAoAjEYja+ZN4cv5q/DyC2LXhh85d2QP7V95kxN7fsMnMITPvl9GkTyfb97rTasuryK1sMDS0qpKnFSDPi7jlzy2awsp8bcBUBQVsvuXn5n601ZEIhHTRw2kYauOyGxs2PrTXL5fu0sQoFYt4vif23hj6CjqNmlJ3xGfIpFI2b56Efu3rqX/yC+oGdYYF1Ft5E/N2fzvhYOHbylvlJN3YOlno9HA5s9fJahJx9K6irwsUuMuYufqVa4Po9FA5PZl+IW2qNSYgQ3bERo+gG1flU/Z3vrtsaWfbxz9jdwUIWXy49xWakUhv33VF7/QlpiMRiLWTOfVCT/i5BVI1B8ruXt2P7U79KZai65YO7hw7dB/bzysr6tdOR6pV5uE0K91TWb8fqFcvRAvR+a82445f0Q+s4/KIEuuJPJeBl5ONqVlDjZWjO3VlFM3U8vVlVlI2fRZT96Ys7tKY1SEKu+G8hMrKY4qY+IuPLWeogvbMGpVZP86lsw1I8lc+R4ld86atVUnx5Cz7evS7wWHFqO8ehAAbcYdsn/5jMw1H5Cz5UsMxf9cKnaRSITpoRBjVCuR2Ls9s40suAkiS2uzckuvGkidvM3KrfxDEVsLEraVb10MxTml1/RF2ZTEX8S24csP9ru5+yeSTu0o/X57/xrij21Gr1FxfuknnJozlJMzB5Nx7bRZ29y7V7i0Ylzp92u/LyDl4n4A5Cm3Obd4FKfmDuPC8s9RF+a+9LmXFGSTFXeOgNavV3hdV6Ik9240XvWfTb7pXqsZUqvKE9oWZybjVrMJAPZeQajyM1EX5Ve6/cvAysg8dsUVln5fH53PtmtyVDojX+xPZ8QfqQzb8YCzyUqztjHpJXx9KLP0++JzuRy8WwzAnRwNn+5NZ+SuVMYfyCBPpX+p83a1kVLTTeC8srEUE+hsQY5SGGP3zSLebuiIpUQQgpytJc/d178B8ftWkHp2Z+n3xENrSTm5Bb1GRcyKT4lc+B6X5r1Lzo0zZm0L4q9wdU3ZgerOzoVkRArrq+jBba4sH83lH94nduUXaIpe7vpSZSXjEFAPiaUMsUSKU7WG5Fx/uuZZamVTagkxaNXAQ0FWJMKo12LU6zHqdZgMeiztXx454g/Lf2bL9rIkA8tXrWPdr1tRqlS89/Hn9H13OL0HDeX4KfN7HBkdw6gvyvhevp+/iF37hHdO3K07DPlwDP2GjGDkmHHk5L78Paxp44Y4OjhUur6NtTVNGtbHysqyXHlObi4KpYoGYfUQiUT07tm9wt/7OFxdnAmrWweptPwaE4lE2NoIBw29Xo9erxe4IaFUkDKZTKg1GkQPn3GLpo2xlgla2xo1QjAZy07n21cv5vjusqQCf25cwaHfN6IuUTH/y5FM++gtpozoS8y5k2ZzvB17mcXflAnbvy6bxdnDwsEm+e5N5ox9n+mjBrLwq4+Q5+WYtX9R5Odkce3SGdr3LFOOKYrkSKUWePkFAVCvSSuizxwHQIQItUqFyWRCU6LC1t4RseTpe1hlcOnkIVp0fgWAG1HnqdekJXYOjtjaO1CvSUuuXz6HyWTCZAKNukR4PioFTq4CiWpo09ZIJIJuPKROfQpysp881u/LiTu2vfR71K5VXD34Kzq1in1zP2bnt++yffIgkq+Y7wfpt6I5tKjMWnp203zunBH4lnKSb7F39of88e0QDiwYg0r+8tdT6TxuXsbBww97t7Kz1oWti2gxYAyl+9JDxB39neAmnbG2d65U357Vw0oJe5+EhEtHqN6im1l5UtQJ/MNaIbWSoVYUIpZY4OQVCIBvveYkRZuvgargx4Ox7LhQxnuz+ug1Np++hUqj45PVxxmy9CCDF+3ndFyqWdvohCzGbYgo/b5g92X2RQkKm9up+YxaeYyhyw7y2doT5BaVvNA8K0KjEA8crC3NyoM9HAl0r/we+Sws3neFT15pxOP/Axc7GXX9XZGK/z7la5UtUzZ1OyM/shz7psLmo7oVgfug+Yiklrj1/w6xlS0GlZzs9R8jq9mmUqRyJoOegsNLces/E4mtE6q4ExRGrMGlV3nSM+X1oxRfNM8EI3X2xa3fDLNyc4jI2fIlIMKucS/sGvcCwKnbJ+Rs+RL5sRWACY+hy5/ay4tCEbsfWbUyc6X8yHKcunyIUaN66WP5Nu7CjZ2LCe7QD4D0mOO0Gr0YsdSSZiPnYmFti0Yh58yCEXiFtavU8zIa9FzfvpDmH8zDyt6ZtOhj3Nr7M43emVyuXurlw8QfM8/sZ+vuR7MRz3YTubFzMXXf+KTUpe+vyLx2CrdaTbGwrryQVBFu7V3JnYPrcK/VlDqvf4zEwhJH3+pkXI3AtXpDCpLjKMnPRC3PRubw9zFo/xWdQuxYfiGPPvUEt7aTiUoWvOKNpUTE9129sLUUI1cb+Hh3Gm0CbSr17PRGE0vO5zKrmxdO1hJOJChYfTmfrzt4lKt3NL6YbVcLzdr7OlowI9yz0r8ho1jHvVwNdT2Eg1hqoY5rmWrWXC7AUiJiVEsX6rhXzrT+177+DfBs2IV7fy7Br62gxcy+eoKGH/yAWGpJ2Huzkcps0SrkRC/5ALeHVp1nwWjQc3fXIuq/PwdLO2eyYo6ReGAVdQZOKlcvM/owKSe3mLW3dvMjbNjMp45h6x1CwsFV6JSFiC2syLt1AQf/ZxPZ5lw7RcKBn9EWF9Bg5AIAHINCcaremHPTXseECb+2fbH1DHpmX5VFj66dmfPDMt7uL5B/Hzp+ktVLF2BlacmyeTOxs7OlQC5n4Puj6Ny+cvdYp9czc8Fili+YjYuzEwePHmfxijXMnPJ1uXp7Dx1h3Sbzd06gvy+L53z3Qr9ry/Zd7DlwmHp1ajPhs9E4Otg/sW5Wdi6eHu6l3z093MnOfv7DqsFgoN+QkaSkpvF2vzdoEFpGvjppxmzOnL9IteAgJlRAMHw84jSDh5eR6zbv2J2tK+bTpfdAAC6fOsK4OSuwsLTkk2mLsLa1o7iwgJlj3qVh646V26f0OjYvn8OYGYtxcHIh8uQh/li3jPe/LP+ev3B8P4d+N8+y6OHjz+hvF5qV/xVbf5pH/5FfoFaVKaTsHZ0xGgwk3YkjuFY9ok4fJT9bUEx1fmMgy6Z8yti3wlGrlHw0eR5isaCT1mm1TP94EBKxhJ6D3qdxm84VjvlX5Galk5uZRp2GwplAnpuNi3uZdcXZ3RN5bjZSqQVDPvuGqSP7YSWzxtM3gHfGTDLr7+yhP2nesfsTx6vWoisXtvxAvXDB9S/x8nF6jluKxMKSbp/Ow9LaDnWxnD+/e5/ARu0rt2fp9Zz/dQHdPl2AtYMzCZeOErlzBR2HTylX7975Q1w7uMmsvYOnP10/mWNW/iTEXzpKtZZlwkzylVPYOrvjGlCzXD1lQTbJVyJ47asVnFp7s9L9Pw3FuRkU5aTjU7ep2bWES0cI6y4QMMvsnTAZDeQk3cQ9uC5JUSdQ5Ge90Njh9QNYvO8K/VoJv/P49RSWvN8JS6mEue+2x1ZmgVypZsRPR2hX17dya81gZOGeKOYNaY+znYyjV+/z8+GrTO5f3up9KCaJzadvmbX3c7Vn9jvP9g56HqTnKxiy5CC2Mgs+7FafhsEeT61/Oi4VdwdravhUTnB+maiyMGXpVQODsgBDcS4GlRyxzB6powcmg57Ck6vRpFwDkQhDcS5GZT4Su2ezLuvyUtBlJ5Gz5aEVxGhEXEE727Cu2IZ1reqUS+ExdBlSB3cMygJyNo9H6hqALLABiujdOHUdjU2dDqhuniR/3zw83vnhucd5GtTJMShjD+AxdBkAJffOI7Z1xtK7FurkmJc+nqN/LTTFBajlOWgUcixs7LF29sRo0HNr7wry4mMRicSoC3PQFOcjc3j281Jk3ac4I5ELyz8DhLgmmaO5JsevWXf8mj15U38aMq+fxcreGaeA2uTevVJhnbToowS0qthqVVnUeX0UVg6uGPU6rm6dQ/yxTdR6ZTjVuw7hxo5FRMwegoNPNRz9aiIS/7NuLTXdrChQG8hV6pGrDdhbifGwk6I3mlh9OZ+rmWrEQI7SQH6JAVebZy/nFLmOpAIt4w4IRMIGk6nCdl2r29O1+pMPd5WBSmdk6rEsxrRyw9ZSXDpekdrIit4+3M7RMO1YNtsG+j9z06+or38D7P1qolUUoCnMQauQY2Ftj+zh+krY/zPyxKuIRCLhenE+VpVYX6rsFJQZicT+LLgKm4xGLCto59WkO15Nnm992XoGEdhpMLErv0BsKcPetwaInn1f3et3wL1+BwoSYkk8uJpGo5agyklFlZVM628Fj4XYnz9HnhiLU0jD55rbX1G3Vk3yCwrIzsklv0COo7093p6e6PR6Fq9YRVTMVUQiMdk5OeTm5ePu9ux7nHw/hXuJSQz/RHDdMRoNFbbr1aMbvXqYa6BfFAP7vsGo4UMRiUQs/Xkt85b8aCbI/Z2QSCTs2ryOouJiPp0wmXsJidSoFgLArKkTMRgMzFywhINHT/BmrzI3mz0HjxCfmIylhQUgkPYG1qhDsTyfgtxsigsLsLV3wMXDC71ex851S7l77QoisZiC3GyKCvJwdHm250fmg2TSkuNZ+NVHgBDX5ORq3q5Vl1dp1eX5PDpiL57CwcmFoJp1uR17ubRcJBLx4eS5bFsxH71OS70mrUutT3FR5/GvVpsvF6whO/0BC7/6kJphjbG2tWP+loM4u3mSnZ7K/C9H4hdcAw+fZ1NPRJ48RNN24c+0cOn1Ok7u/Z1pP/+Gu7cfm5fPZv/WtfR654PSOns3r0YskdDyKffELbAWJUUFKAtyUBcXYGVjj52rJ0a9nsgdK8i8E4NIJEJZkENJYd4zrTQA8sz75KcmcmC+YGU0mozYVHAmqNG6BzVa93hmf0+DQa/jfsxpmvf7GAC9Rk3Mvg28On6ZWd3zm3+gef9PXuq7O+HSEUKadkYsLv+8VPJc8lMT8A9tBQj/oy6jvufC1kUYdDr8QlsgrsQe+zTU8nUhX6Emp0iFXKHB3toSTydb9AYjKw7HEpOUg1gEOYUl5CvUuNqbe0/9FfdzikjIkvPpWiFO3mg04epg3q5Ho2B6NPrn4rHdHKzZ/fUbONpacTs1nwmbTrP1i1exlVlUWF+t1bMhIo6lwzv9Y3N8HM8VM2VTpyOqW6cwKPKxqStMXHXjKAZVIZ7DVyGSSElf9paQ0OExiMQSMJW5Bzx+3cI9CM/3fnrquC9qmZI6CJo9ia0z1rXaok2/hSywAcprh3HqNgYA6zodyd9nzm78MqDNSiB/33zcB81FYiNYGzQPbqC+e470+IuY9FpMGhV5f36P6xuTn9Fb5eHTqDPpsSfRFOXh2zgcEKxGWoWcDl9tQCyRcnRqH4y6vzwviQTTY8+r9LrJhL1XCO3Gr37quC9imcpPvEbm9TNkxZ3HqNOiVyuJ3jiNJkOnAaBRyClIvkmzkZXXZlWER0KgxMKSgJavkXBcmK+FtS2N3hWegclk4ti3b2Lj6vtCYz0POgbbEpGkJL/EQKcQIUnG0XgFcrWB1X18kYpFvLU1Ba2hvFO8RCzCSFnZo+smIMjZkhW9n/5bXtQypTeamHo0i/BqdrQPLrMcuttKaR9si0gkoo6HDLEICtVGnJ7i7vekvv4t8GjQieyrEWiL8/BoKAQ3Z0UfRqeU02zsOsQSKee/64uxov3QaCz9XnrdZMLWK5imn6166rgvYpkC8GnZC5+WgnU+Yf/PWDk9Xev3OJyrNeRWXjpahZyc66dwCKyH1EpwHXOt3ZLC5LiXJkwBdO/SicMnIsjNy6dHV0Hjv+/QUfIL5Gz/ZQ0WUinhvQeg1Za/xxKJBKOp7B5rNMJ1k8lE9eBgtq5bwdPwd1mm3FzLLNz933iNUWOfLkh5eriRlV3m5paVnYOHx7MPuc+Cg709zZs04syFS6XCFAj3rWfXzqzdtLVUmDofGcWq9b/w61rz/2XT9l2JOnOUovw8mnUUhM+Lxw9QLC9g6oqtSKUWfDn4FXRaTbl24r+8Y3SPnp8JfAOr8c0ycyvG43gRy1T8jVhiL0RwLfIsOq0GtUrJqtkT+WDibKrXbcDExRsAwe0uM/U+AGcP7abnoPcRiUR4+gbg5uVLxoMkQmqH4ezm+XBsP2o3aEpK/O1KC1PvfFpmYXJy8+DO1TLhriAni1oNmvEg/k7pbwNo1qE7B7aVxeedPbybaxdPM37+qmcqp0KadSEp6gSqwjyqtRAU1PcuHEJdXMCb035BLJWyZVxvDH85E/z1eRl0D5+nyYSzbzBvTFnH0/AyLFMPrp3HLbA2No6C8qMoO5XinHR2TBkMCNaond++S5+p68lNvsXxFcJ7XK2Qk3LtPGKxpFysVVWRcOkobd6dYF4eeYygxh0RS8uO1Z7V6/P6JOGclHrjIoWZKc897iN0CQvgxPUH5BeXEF5fcCE8FJNMgVLDxjE9kErEvDFnNxpd+XhkiUSE8bFnp9EJ+6IJCPF0ZM3HT1fM/dOWKUupBMuHLsq1/VzwdbEjJbeoNEHFX5GaryAjX8E7iwU37pwiFUOXHmLdJ90rJVS+KJ5PmKrXifz9CzCqCvF4V0jUYNQokdg4IZJIUSfHYCg0N2dKHL3Q5SYLQoNegzrpClb+YVi4+mNUydGkxmHlVw+TQY8+/wEW7uWl4BexTBm1JWAyIbaywagtQZ0UhUO7IcK87FzR3I9FFtQITfIVpC5CELYm7RaKqF249jY3pVcV+sIs8nZMwbX3JCxcyzZYp84f4NRZ0Cypk2MovvjbSxWkAHyahHN1y2y0ikLafC4IrPoSBVZ2zoglUnLvRlOSn2nWzsbFi+LMJAw6LUadhty7UbhUa4CdZyAaRQH5iddxCQnDaNCjyE7BwTukXPsXsUzV7f0xdXsLmqfcu1dIOL65VJACyIg5gWdoGyQWVqVlBclxJJ3eQeMh31Z6HHVhLjJHN0wmE5nXTmHvUw0AnapYiCeRWpByfg+u1Ru+sDvh86BziB3zz+RQqDaytJfgH67UGnGSSZCKRVxJLyFTYR5D5GUn5X6BDq3BhEZvJDqthDAvGQGOFhSqDdzIUhPqKUNvNPFAriPYpbwv84tYpkwmE3NP5RDobMFbf8my1zbQlpj0Ehr7WPNArkVnNOEoE5Oj1DMrIptFr/pUuq9/CzwadeH273PRKQtpPFpwEdarlVg+XF8F96JRF5ivL5mzF8qsZIx6LQadhoJ70TgF18fGIwCdQk5h8g0cg0IxGvSoclKw8yq/vl7EMgWgLS7A0t4ZdUEmOddP0eSh8JZ6Roix9GvXr1x9VU4q1m6C60hx6h2Mei0Wto7InD1Jv7gXo0H4H8oTY/Fv/3KzR/UI78y3s+ZRIC/kl5VCljSFQoGLszMWUimXoq6QnmF+j328PUlIuo9Wq0Wt0XDxcjSNG9YnKDCAfLmc2Gs3aFg/FJ1eT/L9B9SoVv6d83dZpnJyc3F3E4ShYxFnzMb9K9zd3LCzteHq9Tjqh9Zl94HDDB4guD1u/l2I2Rs8oO/TuihFfoEcqVSCg709arWG85eiGDHkbUwmEympaQT6+2EymThx5hzBQQEA3Lxzl+mzF7ByyXycHB0e2qTK0Lxjdzb8MANFYQFf/SAcqEuUChycXJBKLbgVG0leVrrZXNw8fUi/n4hOq0WnVXMr5hI1Qhvh5R9EcWEB8TevUr1uA/R6HVmp9/ENql6u/YtYpvqN+Ix+IwTvituxlzm0fSMfTBQSPhUV5OHg7IpOq+Xgb+t57e0RALh4eHHzyiVqhjWmsCCPzAfJuHv7oSwuwtJKhoWlJcWFBdyLi6XHW8MA2LFmCcG1Q2nS1jyLXEZKEkpFMdXqNigtC23amj/WLUVZXARAXPQF+g7/DJ1WQ/r9RIrk+Tg4uRAXfQHvAOF/cz3yHAd/28BXP6zFSvbsg2NIi3DOrJ+FulhOr4krAdCWKLC2d0YslZJ+KwpFXoZZOztXbwrShDOBXqsh/eZlvGo0xNE7EHWxnKz4a3hWr49Rr0eedR8X32rl2r8My1T8xSNUf8zFz8W/OkOWlWXu3DKuN29O24jM3olBC8oSC0Ssnk5Aw7algtRvX/fnrTllsWOVgTw9GY2yGM/qYWbXEi4eoXn/j8uVlRTlY+3ggkGnJXb/LzTq9V6VxqsI4Q0CmL0zErlKw4oPBOW4Uq3F2VaGVCImOiGLTLl5HLW3ky1JWUVo9QY0OgNRCZk0CHIn0M0euVLD9fs5hAW6ozcYScktIsSz/Pv2n7ZMFSjUONhYIhGLSctTkJpXjI+LoFCe/tt5+rWuST3/MoVSdS8nDk4p2wPfmLObDWO6v1CGvqrguYQpC/dgTBoVEns3JPaClGgTGk7ub5PIXPkeFt61kLoGmA/m6IFN3U5krnwPiZM3ll5CGlGRxALXvtORH16GUaPEZDRg37yfmTD1IjAqC8jdLvjvmowGbEO7YF1NyO7i8up4Co4sB6MBkdQSl1cFd0NDURYiqVWF/WVtHIM+LwWTtoT0Jf1wfm0C1tWaUxy5k+ILWzEo8slcNRzr6i1weW0CRWc2YigpouDQwzSYj6Vm/7vh4B2CXq1C5uReaonxbdadyJ+/5OTMwTgF1MHOM9CsnbWzJz6NuxAxazA2rj44+Al+umKpBc1GzOL69kXo1QpMBgMhnd4yE6b+TqRFH6NGt3fLlZUUZJUTrh7H2UUfoci6j16j4sjk12n49iQ86rbkysZpaIoLAHDwq0GDgYLGqTgzmZhN34FIhL13MA0Hv7hA/TwIdrFEpTPiZispdccLr27HxMOZDNvxgFruVgQ4mZu9PeykdAyxZdiOB3jbW1DDTRCWLCQipod7svR8HkqtEYPRRL8wRzNh6kVwPUvDkXgFIS6WDN8pBMKObOZCywAbetayZ+7pHIbteIBULGJSBw9EIhF5KgOSCrSpT+vr3wI7rxAMahVWDm5YOQjry7NxN66tncClee9i718bGw/z9SVz9sSjYWcuzXsXmYu34GqHsL5Ch33P3V2LMZQoMRn1+LV/y0yYelFc3zAJnaoIsVhKzTfHYfEwQY4y+z6OwfXN6udciyAz6iAiiRSxhRWhQ2YgEonwaNCJgntXiJw/BEQiXGu3wK1e25c61xrVglGqVHh6uJcKIa/16MrHYyfSe9BQ6tWpTUiQ+TvH29OTHl068fqgofj5eFOnlnCPLS0sWDxnBrMWLEGhUKI3GBgyqP8zhZqqYvzk6URGxyCXF9Lptb58MvI9+vZ+jQXLfub23XuIRCJ8vb2YNnF8aZvw3gNQKJXodHqOnzrL6qULqR4SxJQJY5k0YzYajYZ2rVuUZvJLup9Co/rmh7uc3DwGDPsAhVKJWCRm07Yd7N32Czm5eUycPguj0YDRaKJHeCc6tmuN0Whk0vRZKJRKTCaoVaMa334lvAsXLF2BqqSELyZ+S2BgIN/NWcDj+at8g6qjVilxdvMoTYrQsktPlk7+lCkj+hJUq27pwf9xuHh40axDN6aM7Iu7lw8B1YW4PamFBR9PXcCWH+eiUiowGvR0ffMdM2Hq78Kh3zdy9dJpjEYjnXoNoE4j4azQ650PWDd/ClNG9AVM9B/5OfaOzsTHxbJx0XeIxGJMRiM9B76Hb6AgSKQm3aNh644VjnPp5CGad+xezpJk5+BIr8Ef8N3otx+O+SF2DoIny+vvfsjcse8jkUhx9fRm+JeCdXTz8tnodNpSt8hqdcIY8vkUngQX32po1SpsnD1K3fhqtOrBocVj2T55EO5BdXDyDjJrZ+fqSUjzLmyfPAh7Nx9cA2sBIJFaED56Duc3L0BbIpwJQrsNMhOmXhQ6TQlpcZdoP2ziC/WjLpYDFac4vPjbUhIuHkGvVbP5i9eo1f51mvYRFN7xl45QrUVXM8tfcU46ivwsvGs1Lld+9cCvpFw9i8lkpG6nvvjWbfZC8wYI8XRCpdHh7mCN20N3vO6Nghi/8RSDF+2ntp9rhQkdPJ1s6VI/gLcX7cfHxY6aD+OKLKQSZg1uxw97WMprMAAAIABJREFUolCodRiMJt5qW8tMmHpRTNl6jiuJWciVGnrN2sXIrvV5vVk1Im48YOGeKORKDWM3nKKmtxNLhncmJimb1UevI5WIEIlETHijGY42wvkuPlOOu0PlzwB5xSUMW3YIpUaHWCRi29nbbBv72hNdBp8HIh8fn6cmzbS1tSXm2g16b//nsuu9DOjlGeT8NhHvDzc8dx/yYyuwCeuGpefL3RCqgrw9s7Gu0aocz9SOPk60bdmMgoICs/pdu3bl8+k/sPmu2aV/NVR5GVz6eTydvjF3C6ws4nYtw6/5Kzj6/v0v3KNT+9B+wvpSnqk2/lYUXv6TSV+Xmf9jLl9AtH8ahkJzDd9/EzKKdUw8nMmGfs92aXkS/ogrxNNOSpvAqln/1kfnY20hfiGeKYvOnzJr8zG2bTN353ocl6JjWXlLQq7K+NR6/0aU5Gdwbc2XtJjw7FS8V9d8SdiwWYilL+dFc+XHT6j++uhyPFPNfa1wTj3L6A9HmNWPjY3FRSYC49PT5v/bkJaewaixX5fjk/onMOqLr1gy7/uHcUz/AMRSdDInojP/ap/6dyM3M40lk8fw3Zo/nl35JWLhVx8xbu7Pz674N8FFpEJusuZ8qvbZlf9FKM5J59DisfSf+fR9+XlwP/YMxTnphHZ9Orfe34n0W9FcO/QrPb5YxNu1pdjF7USfk/Qfm8/LRHq+gvEbT5Xjk3pZUKp1zNx5kVmDX55rYWWtWLJa7Vl15Crz5puHAv17IrlfNkQSTGolmauHP3cXTuGj/rOC1K7v0dy/ikj68qwG/1aIxGJ0JQoiZg957j7q9RnztwtSBq2aiNlDMBn0lcqU838BEpEIhdZYajl6HrxZz7HKgtSKS3kcjVcgk/73bmMvCyKRGL1aSeSCoc+s22DE/JcqSJXkpSMS/1dTGgIglohRKBT0Gfz+PzruikVz/zlB6n8xxGIJKqWCbz/8ZwlM/5OC1N3rV7hwfP9/bPwXgUgsQVuiYOfDWKiXicCG7f6jglTCpaOc3TQPS5uXlxL83wSJWIRCrePdJQdeet+2MouXJkipdXreXXIAg9H4wue5f/QU8oiT6p+A1NEDn8+2m5H2Po7c378ha/2ocmXKqwcxFJelnM3fNw9dTvILz0d59SAFhxZXqY1rn8n4jNmGdY3WLzx+ZfCIg+o/AWtnT7p9v7scaW/kqq84s6C85jrl4n7U8rJA7NjNsyjOeHFtTsrF/Vz7fcEz6z0i7e02cy+Wto4vPC6U8Uf9p3HwbjEdVyeSkFcWJD5sxwMyigUN9JX0EkbtTuObI5kcjVeU1vGwk7Lj7cBS0t4nQW808fqmZFZGlrdyb4opb2H9eHdapeY7qoUrW94K4I26Fb+QZkdkE5GoqPDa34lHXFP/Flxb9zU3Nk6mzdRdpaS9GZH70RSWraNbv81Gmfni6ygjcj93dpYF/jcevZw2U/8odWH8u/CIe+o/CW9PT8Z8NIK78QncuZdQWv76wKGkpQvW6UtRVxj0/ijGfPkN+w4drfIYer2eNt168cPy8gf0levLB/W/Pbz8e+15MWn6LA4fj3gpfVUFj7iqXiZcPLxYuPVIlYh1H2HZ1M/5/pN3ypWdPbybgtwyPqf1C6eRdj/hr02rjLOHd/PrsmdTh1QGNcMa0+WNgaU8Yi8Tj/ip/i7YuXoy+Id9pYS4z8LhJeP5c0Z5RcadM/tQFpTtc6fWfU9BWuJfm1YZd87s4+ym509UVq1FVwbM+o3OH06vUrtHvFL/aeyLSqTVxC3cyyh7d7+9aD/p+cL7NjVPgYejNT7OdhyOTa5y/3qDkR7f7eTHg7HlyjecjCv3feRPR6o++cfwiLS3WXUvohNeLG39/1mVrlFdjDbjLka1En1BWWCs8uqhcsKUy2sTsHAP+g/M8P82dKpi5Cm30ZUoUOaWHa4fXNxfjiC44eBJ2Hv/c0GR/+1wt5WwKbZiwa6xjzUrevsys5sXXavbVbnvqLQS/B0tiEhUlssItfkv4/30jEyD/4/KQ1dSTHHqHfRqJSV5Zeso4/LBckTAdd6aiK3X/6+jlwFPD3dWrv+lwmstmjZm67oVLJs/k9d6VD2Z0vnIKIIC/Dl8PKLcGlq1ofyhdsvap2cp/H9UHipFEcn3blKiUpCdXmZ9P3d4Tzki4ffGTSuNk/p//LPQKIvJvX8bbYmCouyyfe7u2X2oHlO+dnh/Ms6+/1xs938zPBxszISbR2hSzZM1H3dn7pD2dG8YVOW+I+Mz8Xez58T1lHL73Ma/jLf645efHOh58bf5XiivHab44m+ACAuPEFzf+KbcdcWVfShi9oJBj9TFF5fekxBbyFDdjKDwzAZEIgliK1s8hi5Fl5NE/t65mAw6MJlw7TcDC5ena8GfBdXtM8hqtkZi64wq7gQObd9BdSsCbcYd8v6cicjCEo9hP5G7dQJO4aPQZtxBX5COU7ig8VNePYg24w7OPT4n9/dv0BflYNJrsW/et5QMWBF7kOLzmxHL7LDwqIbooeuMXp5B/t55GEsKEds44dLrK6SOlSdCfRl4cOkA8ce3IBKJcPCpTuOh5TPg3T+3m/vndmM06LB186PR0G+RWspIv3KcOwfXIRKLkcrsaPvFCooyEon99XuMej0mk5FmI2Zj5/H88TMAGVcj8Apri5W9C2nRR6nZfRjpMSeQp9wmeuM0JBZWtBu3ios/jaVenzHIU26hzE2jXh8hxX3Kxf3IU25Rf8B4Ild9RUlBFkadluCOAwhq+4ZQ58I+7h35BQsbOxx8a5S6NqnyMojdPBONQo6VnRMN35mMjYvXE+daFRy6W8xv1wsRASEulkzuVD4d9d7bRey9VYzeaMLXQco3nTyQScWcTFSw8UoBYpEIW0sxy3r5kJSvZc7pHPQGE0bgu3BP/BxfzN2nVYAt1zJLSJFrCXAq71668GwOd3I0aPQmOoTY8n4TIb1zdFoJP13Kw2A0UdvdirFt3bGUmGtCj8cr6FvPkd23iojL1hDqKWNlZB4ag4nhO1MJcrZkSmcPeqxP4tB7wUw/nkW3Gva0ephsYnZENq0CbKjlbsXMkzmo9ULs0udt3Aj1lGEymVhyPo+otBI8bKVIH8u2Xtk5VhUZlw+SErEVESJsfapRb/DUctfTLuwh/eJuTHo91m6+1B08FYmljOzYEyQdWYdIJEFibUuTT35CkZnIra2zMBl0mEwmwobNxMb9xdZRzrVTuNVtg6W9C1kxxwgKH0r21ZMUP7jNzV+nI7awosmnq7i6ehzVXx9N8YPblOSmUf11gS8mI3I/RQ9uU6vvOK6t+xqNPBujToNf+wH4tuoNQHrkfu4f34RUZoedT/XSdVSSn8GtbbPQKQuxtHOizsBJyJxffB3t3n+I9Zu3IUJEzRrVmDu9fPbT7X/uZfuuveh0OgL8fZkzfTLWMhmHjp3kpzUbkIjF2NnZsmnVcu4lJDH5u9lodcLetXjOdwQFvNg979i2NVExV0m6n0JwYPmkGNPnLOTGzduoNRq6denImA8ETfqFyGjmL/0Jg0FPaN06fPvVWCwtzd27Dxw+zjtv9eO3P3YTe/0GjeqH8cPyn9FoNPQZ/D7VQ4KY/91UmnToTvSpw4z7Zhqvv9KdDm0F7ptJ02fRoW1rQuvU4qtvZ1KiLgFg8pef06h+GCaTiZkLFnP+UhRenh5YWJQdER7N0c/fj1nzfuBl6WLPHdnL4e0bQSTCP6QGI78ub5U5tX8np/bvRK/X4enjz4ivZ2Ils+byqSPs2fQzIrEEG1s7vl60nrTkeNbNn4per8dkNDL624V4+pknf6kKos8cp2HLDjg4uxIZcYjX3h5B1OmjJN+NY9XsiVhayfhm6S8smjiaAR+OJfluHDnpqQz4UOAwO3t4N8l343hnzCSWTf2c/JxMdFoN4X0G0/E1IWPmmUN/cmDrWmzs7PEPqYXUUlhDuZlprFvwLYpCOfZOzrw/fgaunt4v9HueB3fP7efawc0gAhe/GmYWlVsRf3L71C4Meh0OHv50/mA6UisZiZHHiN69BpFYjKW1Ha9PWkV+WgKn1nyHUS/sc10/mYOjl3nymKogKfokgQ3bYe3gQsKlIzTq9R6Jl4+Tk3yLEyunIrWwoveUtRxc+DktB35KTtItirLTaDnwU0CwNOUk36Ltu19yeMl4lPnZGHQaQrsNpE7HPg/r7CV23wYsbexx9a+B2EJYn8U56Zxa9x3q4kJk9k50HDEVO9cX3+cORCey+cxtREB1byemvVXeQ+nPyHh2R8aj0xvxc7Vn2lutkFlKOX4thbXHryMWibCTWfDzR11JzJLz/faL6AxGjCaY/U5bAtxezMWwTR1fYpOyuZ9TZJbwYu6uSG6l5qPRGegc5s/IrkJCo8vxmSzbH4PBaKSOnysT+jQrTX/+OI7GJvNWm1r8cfEe11NyqR/ozo8HY9HoDLy75ADBno7MGNiGTlN/5+SMAUzecpZXGgfTpragiJ3x+wXa1vGltq8L03+/QIlWyDQ7vndT6ge6YzKZWLgnish7mXg42WAhKdvLKjvHv+JvEaZ0OUkUnd2Ex7DlSGycMJQUmdWxrt0Ou8avAVB4cg3K2APYN3uTojMbcR80H6mDO0Z1MQCK6D3YNeuLbVhX4aBhNA8Az/1jOvo88xz+9i0GYFvfPH2wKu44ju2GIrZ1Jm/HVBzavoNNnY4oLu8SYqV8av9lvh3I3vBxqTClunkShzaC2d+511dIrB0w6jRkrfsQ69rtwaCn6PR6PIevQiyzJXvTF1h6CfE8BYeXYlu/O7YNeqCIPYD88FLcBjybF+ZloSgjkbuHNtB23Cqs7JzQKs35hLwbdCSwjXBYurV3JSnn9xLSsT93Dq6j5ehFWDt5oFMJz+f+mV2EdHwLv2bdhQ2ygsDxqHWTUWSZP59qnQfi36KnWXlq1FFqvfI+VvYuXF4zkZrdh+HTqDNJp3ZQr88YnALrlKvv3bATZxaOLBWm0q8co0b3YYBgvbK0dcSgVXN6/nB8GnbCaNBx58Aa2k9Yj4W1HeeWjsbxYbbC69sX4tf8FQJavkrKhb3c2LGI5h/MrcIdrhhJ+Vo2xcj5sbcPTjIJRWrz+9Q+yJZetYWNac3lfPbfLqZvqCMbrxQw/xVv3G2lFGuEdrtvFdEv1IGu1e3RGUzlOCQeYdrxLB7IzQPF+4c50qOmeepzsQgG1nfi11g5kzqWF/RGNnXBQSbBYDQx9kAGCXka/BwtmH0qm0U9vfF3smTmyWx23yyif1h5F0iN3kh0egnj27mh0Bo5Fq8g1FPGh81d2RVXVKGLYKcQO04mKmgVYIPOYCI6vYSxbd0wAQt7emElFZNaqGPGiSxW9fHjTLKKlEIdG/v5UVBiYOiOVHrWtEejN1ZqjlWFIjOR5GMbaTLmZyztnNApzfc5j/od8H1ILJ1wYBXpl/bi364/SUfW0/CDRVg5uaMrEdZR+vk/8W/fH68mj9aR+T5345cpqLLN15F/h4F4N3vFrDwr5ijB3d7D0s6F6xu+ISh8KB4NOpF6dqdZUggA9/odiV7yYakwlRV7gqBwIY6xzluTsLB1wKDVELV4OB71O2I06Eg6tJZmY9cildkR89MY7B669N39YxHezV7Bu1lP0i/t4+6uxdR//8V44e4lJPHzul/YsvYnnJ2ckBea3/OuHdvT/w1BobVkxWp27t7PO2/1ZcXaDaxeugBPD3eKioV7/tsfu3lnYD969eiGVqfDaDC/52MnfUvS/Qdm5cPeHkDvV83TPIvFIoa/O4hV6zcxe1p5JeJno0bi5OiAwWDg/dFfcOdeAkEBfnwzYxbrflxMUKA/X387k207/2TIoPKxPRqNhguXo5g2cTzFCgX7Dx+nUf0wxn7yEZu372LXZnOOn1fCO3Po2Ak6tG2FVqfj4uVopn41DhMm1i5fiJWVFckpD/hy8gy2/7KaYxGnSbr/gL2//UJefgG93hrCm71eRaPRlM0xOJiX5TyblhzPvs2rmLT0F+wdnVEUmb+LmrTtQodXhZTHf6xbzpmDuwjv8zZ7Nq1k7JwVOLt5olII/4OIvdsJf3Mwrbq8il6nw1jBu2jFd1+W8kY9jm5936VNt15m5ZdOHuL1dz/EwdmFH6eN47W3R9C0fVeO/7mNAR+OJbhWvfLzbRfOrE+HlApTkRGHS1Orvzd+OnYOjmg1ar4b/TZN24Wj1+vYvXEFU1dsxcbWnnnjRxBQXciSt3n5HNp0e5023V7nzMFdbPlxLmNmVC1c4EWRn5ZAzJ519J68Fpm9E2qF+TMKbtqROh0FJeXlnSu4fXo3oV3fInrPWnqOX4qtswcapbDmbp34g9CuA6nRugeGJ5wXjv00icIM82cU1uNtarYxT2qQcOkIjV8fjrWjC0eXf02jXu8R0qwLcce203Lgp7gH1/3LfDuz+/vhpcJUYuTR0rTlHYZPQWbniF6rZtf0YQQ37YRRrydq1yrenPYLljZ27JszqjST4blfF1CzzavUbPsat0/v4dyvC+j+2bNDBp6GxCw560/GsXpUV5xsZRSqNGZ1OtXz543mwpny58NX2XM5gQFtarHu+HUWv98JD0cbikuEpCN/XIxnQJta9GgUjE5vwFDBWeGbLWdJyTHfTwe1rU3PJubWPLEI3mlfh40n45g6oFW5ax91b4CjjRUGo5FPVp/gXkYBAW4OfLf9IstHdCbA3YHpv53nj4v3GNi2/FlbozNwOT6Lr95sjqJEx5HY+9QPdGf0Kw3ZceEumz4zPy+G1w/k+LUU2tT2Rac3EJWQxYQ+zcAES4d3xspCQkpuEVO3nmfDmB5ExKVyP6eYrWNfJV+hZtAP++nVtBoanaFSc6wIf4swpU6OwbpORyQ2QoYtibW5BKzLSSI3Yi0mtQKjtgRZNSFlpKV/KPl752BTpxPWtYUgM0u/ehSd/RVDcQ7WtdtXaJVye7Py3EIGRT76/FQs/cOEoDOJFG12IpYeTzb/SmydkDj5oEmNQ+rihy43BUt/IR2tInInJXfOCn0X5aDPT8WgzMcqsCESW+Ee2NTthD5feCFrU+Nw6yekNLUN60bh8X82QDX3TjQ+jTqXZqKrKPanKCOB2/tWoVMVY9CW4F77YRr5kPrEbPoe38Zd8G7QEQDn4FDuHd5IiTwb7wYdK7RKNX3/+0rPT12UjzLnAS7VGiASiRBLpBSlJ+Dg82QXCit7Z2zdfMlPuoGduz+KrPu4hAjakMSI7WReOwUI6dMVOQ/QFOXhWqMRVvZCelDfxuEoHh5SC5JulJIB+zV/hZt//ljpuT8NV9JL6Bhii5NM0HI4yMy1HUkFWtZcLkChNVCiM9HcT0h9GuYpY/apHDqF2NI+SEjUUM9Txq8xBeQoDLQPtq3QKjWtS9UtnuHV7fg1Vk5GUXkh7GSikr23izCYIE+lJ1muwwR421vg/9CK1aOmPbtuFpoJKhdSVDTytsZKKqZDsC2/xBQwppUrEvGTrUMt/K1ZdiEXrcFE5AMVDbyE9gqtkcXnconP0yARiXhQKMzzamYJXarZIhGLcLOV0shHyMzzoFBXqTlWFQX3ovFo0AnLh+vIwtZ8n1NkJJJ4cBX6EgUGbQkutZoD4Bgcxs1t3+PRoAse9TsA4BAYyv1jG9HIc3Cv36FCq1TokMoTxWqL8ynJScUxWFhHIokURUYidk+hMLC0c0bm6kNh8g1s3P1RPZYm/cGZ7eRePw2AWp6NKucB2uJ8nKs3wtJOWEceDbugyhHWUdH9G4S9J1gZvJr2IGHf00nZK4NLUVfo3qUTzk7CPXdyNL/n9xKTWLJiDcWKYlSqEtq0FO55o/phTJoxmx7hnejasT0ADcPqsXL9JrKycgjv1L5Cq9QPs6oW1wDwavdwVq7fRGpaeW6lQ8dOsv3PPRgMBnJy80hISsZkMuHr401QoDB271d7sHXHH2bCVMTZCzRv0giZzIpunTrw89qNTBw7BonkyVrTdq1bMOuHpWi1Ws5cuESTRg2QyawoVij4fv5ibt+9h1gs4X6K8G6KirlKz25dkEgkeLi70aJpIwCS7j8oN0epVAq8eFbLWzGRNO3QDXtH4f/zKAX440hLjueP9ctRKYrRqFWENhU09DVCG7J23lSadehWyuFUrW4D9m1ZTUFOFk3adqnQKjVqSuVjXQoL8shKS6FGaCNEIhESqZTUpHv4BT85BtDByQV3b18Sbl7D0zeAzAdJ1AgV7uOxXVu4cu4EAPnZWWSlpVBYkEutBk1xcBIs/c06diProbCXcPMao6f9AECrrq+xffU/K0gBpN+MIrhZF2T2wpqT2Zk/o/zURKL+WIFGpUCvVuEXKqTt96pen4g1MwhpFk5w044AeFYPI2bvepQFWQQ36VShVSr848rHjKkK8yjMfIBXzYal54X81ARc/J58XrB2cMbe3Zes+Os4evkjz0jGs4bA9XXj6G8kX4kAQJGfRWHmA0oK8/Cp3RhrB+F/Wq1FV+QPCXizEq7Tbcw8AGq27sml35dVeu5PQlR8Fp3DAkqzyz1KCf44ErLkrDxyDUWJFpVWT8uagsWyfpA7322/SJf6AXSqJ6zXsEA3NpyII7uwhI6hfhVapWa+XXU6i24Ng9hwMq40VuoRjl9L4c/IeAxGE3nFJSRnF4IJfJxtCXhoxerZJIQdF+6aCSrnbqfRuJoHMgspnUL9WXfiBl/0aoxE/GRLeKtaPizaG41Wb+DinQwaBrsjs5CiUGtZsDuKe+kFiMUiUnIFgT42KZtuDQKRiMW4O9jQpJpwTkrJKarUHCvCfyzFUv6eObgN+B5Lz+oorx5EfV8INHPpOQ5N2k3U9y6StfYDPIevwjY0HCufOpTEXyR361c49xyHLLh8Pv+qWKZUtyIwqhVkLB8IgEmjQhV34qnCFIBNvc6obkVg4RqAde12iEQi1MkxqJOi8XjvR8QWMrJ/+QyT/n9XCtKKEPvr9zQbORdHvxqkXNxP3r0rADQY9BUFyXFk3TjHqXnD6DBhA37NuuMcVI+suPNcWjGW+gO/wr1W03L9VcUylX7lODpVMce+FUgp9WolaVFHcXj96f7ovk3CSb9yHDvPQLzqd0AkEpF79wq5dy7TdtxqpJYyzi3+GKPu3/t8ZkfkMLObJ9VdrTh4t5jYdMENZ1w7d25mq7mQouKDXWms6uNL1+p21PWw4kKKigmHMhjf1p3GvuUJG6tqmQKQikUMCHNky9WyWKaMIh2/XZez8g1f7K0kzI7IRmt4KqtCORxPUHA9S8NbWx8etNVGrqSX0MzvyVwRVlIxDb2tuZyq4kSigi7VhDit7dfluFhLWNfXD6MJuq3796aTvbVtJmHvzcbetwYZkfspSIgBoHb/CRTejyPv5nku//A+zcauw6tJNxwC65J38wJXV4+nVv8JuNRoUq6/qlimsmKPoysp5sL3giuRXq0kK+Yodt4fPnXOno3Cyb56AhuPQNzD2iMSiSiIv0LBvSiafLYSiaWMKz9+gvFfus9Nmj6LZfNnUbtmdXbtO8jlaOGeT5s4nqs3bnL63AX6DR3Jjo2rea1HV+qH1uXUuQt89MUEpn09npbNyt/zqlqmQBA2hg1+izW/lCUiSU1LZ/3mbfy+YRWODvZMmj4Ljbby9/DAkWNcuXqd8N6CkCUvLOJS1BVat3gyd42VlRXNGzfi7MVIDh09ySvdOgOwccvvuLo4s2vzeoxGI43aVT1+65/C2vlT+GT6YgKq1eLs4d3cuRoFwJDPp5Bw6xrXLp1h+seD+PanrbTs0pOQOmFcvXSaRd98wtDPJ5dyRD1CVSxTlyMOoyouYsI7wjtKrVJw6eShpwpTAM079uDyqcN4+QfTuE1nRCIRt2Mvc/PKRb5Z+gtWMmvmjh2OTmtucfjfiFNrptPt0/m4BtTkzpl9ZNyOBqDdsIlkJ9wg5eo5/vh2KG9O30j1Vj3wqBZKytVzHFz0Be2Gfm3Gv1QVy1Ri5DE0qiK2jhcsYzq1kviLh2ne72Oz9o+jWouuJF4+hpN3EEFNOiISiUi/FU3azcu8MXkdUisZe2d/hOFfel74bvtF5r3bnho+zuyLSuRKopBA4as+zbmRksv52+kMXXaIjWN60L1hEPX8XTl3O52x6yP4uk9zmlYv74pYVcsUgFQi5u12ddh06mZpWXq+gi1nbrFudA8cbCyZ8fsFNLrKK16OxN7n6v0c3pgjEC4XqjREJWTRosaT3VutLCQ0DvHg4t0Mjl27T3gDQYmy9cxtXOxkbPqsJ0aTiQ5Tfqv0PKqKv0WYkgU1Inf7FOxb9Edi44ihpMjMOmXSqpDYuWIy6FHeOIbEXiCO0+enYeVbFyvfupQkXMJQlI1Jo0Ti7IN9874YirLQZSeYCVNVsUyp4o7jPmgeVn6CeV5fkEH25nE4dRqByMoGo7akwnY2tdqRefZXdI73cOwsHEhMGiVia3vEFjJ0uffRpAl/KiufusgPL8egKkRsZYvqVkRpmnVLv1BUcSewrd8N1Y2jWAWYE2T+nXCr1YTLq76mWudBWNo5olUWmlmn9GoVMkdXjAY9qZcPY+0kEDEqc1JxDqqHc1A9sm9epESehU6twMbNl5COAyjJz6QoPd5MmKqKZSot+ggtP16ES4hg+VPmpnNh+afUef0jpDIb9BpVhe28GnTg7uGNWKfepW5vYSPVqRVY2NgjtZRRnJlMQbIQwOgcVI8bOxajVRQitbYlPeYEDg/TqjsHh5EWfRT/5q+QevkwLtUaVDheVdHYx5rJR7MYEOaI40M3v79ap0p0RlxtpOiNJo7GK3C3Ea6nFemo6yGjroeMSw9KyFbqUWqN+NhL6RfqSLZCT0K+1lyYeg7LFMArNe3Zdk2OSicITEqdEZlUjK2lmHyVnkupJTT0scbf0YLMYh2phTr8HC04cq+Yht7l56DUGrmWqWb724GlcUoH7hRzPEFBMz8bJGIReqMJaQVWqs7V7NhY2EwQAAAP40lEQVR3u4g7uRomdvAo7c/dVopYJOLQ3WIeyXQNvKzZc6uIHjXskasNxKSrCa9mV6k5Pg+cazTh+vpJBHQYiIWtIzplkZl1yqARyHyNBj2ZV45g5SisI1VuKo6B9XAMrEfe7Yv8T3t3Hh9Vee9x/DNbZpLJPkmGbCQhCSEL+xIU5ForxQUVUEQRFKUIVYtaLcrt9WpVWuvCVdTq9VW9VREpRYuyC25F2YSwKCJLkCWEJJDNZJLJfv8YHEgTBA6IqX7ff56ZOfO8ZuacOd/zPM/v8VYU0+itJtAVT+KQ0XgrivEU7m4Tpk6nZ6p400p63TqTsOQcAGpLC9n04p2kXjYZiz2IJm/7x1F09yHsXfkqjvCdpA33HUeNXg/WwBAsAQ48xfv4Zp/vOApNymLngqdp8FRicTgp2fIBwXG+4ygsOYfiTSuJ7XcJxRuXE5Zy5sdRbr8+TJ32OyaMvZbw8DAqKr9p0zvlqaklOspFQ2Mji5atwB3t+2/ZX3CQnjlZ9MzJYtXqdRwqKaHK4yExPo7xY67hUFExO3fntwlTRnqmAEYMv5SXX3+Tmhrf51ztqSHI4SAk2MmR0jJWrVlH/769SUlK5OChIvYdKCApMYGFS5fTv0+vVvuqrvawcfNWPlg43z+X6u2FS1j83vucn9sfm9VCQ2MjNmvbv/RLh17E/HcWsW37V8x4cLp/f253NGazmQWLl9HU5Btq1a93T+a9/S4jLr+EsvJy1m/cxOXDhrZuY1IyjY2NnI05U5m9B/Dcg3cz7OrxBIeFU/1NZZveKW9NDeGRUTQ2NrD2/SVERPnOAyWFB0jN7EFqZg8+X/8pZYeLqPFUEx2bwNCRN1BWUsSBPbvahKnT6Zla9+Ey7n7sz6Rl+X67hw8V8OS0yVx9y69xBAXhrfW0+7o+g3/Oojl/wRXzFddMuguAWk81zpBQ7I5ADu3/mvztWwHo0q07bz7/ONWVFTicTjZ8vILEVN+Q87Tsnqz/cBnnD72Cte8vIb1771Nu+9kSl9WPFbOm0eOSsTiCfcP8/rV3qt5bQ1B4FM2Njexeuwzn0euFb0oKiEnNISY1hwOfr6a6tIT6Gg8h0fHkDB1DdWkRZQd2twlTp9Mzlb/uPS675xncab7rqG8OH2Tx43cw4JrbsDmCaDjBeS6l74VsWvh/BLt2knutb1hzfW019qAQrHYHFYV7Kcn/AoCY1BxWz5mJt7qCAEcwez5bSWSi7ztyp/Vg97r36DroMnatWUZs117tvt/p6Jfm5r7XVzF2cDfCnHYqa+ra9E7V1DXiCg2ksamZ5Zv3En10Ad+C0ipyOkeR0zmKNTsLKa6sodrbQHxkMGMGZVBc4WF3UUWbMGWkZwrg8r4pzP7nl3jqfPOSPN4GHDYrwQ4bpVW1rNl5iD5d3HSODuVQuYcDR6pIjAphad7X9ElpPZXA421gy94S3pk+wj9PadGGfFZs3kdueixWs4nGpmaslrbnnot7JvHu+ny2HyzjgdED/fuLDgvCbDaxZMMempp9Fwu9UmJYsG4Xl/VNoby6jrz8Yob1Sj6lNp7I9xKmbNEphA4aR8nrd2IyWbB1SsN1ZesVq8MunEjxK7/C4gwnIC6T5nrfD77i/RdpLCughRYcyX2xudOoWj0Hz+crMFksWJyRhAwa197bnpLGikM0VRYTEH9sDK01Ihazw0ndwS9x9riE8iUz/QUojmcODMEW1ZmGI/uwx/vmGjhSB1Cd9y6HXrgRmysR+9H9WkJchA6ZQMlfb/cVoHAfW/8oYthUyhb+iaq1c/0FKM6l0NgupA+7iU+fuQ2T2UxYQld6j2+9WnrG8FtZ9eQvCQgOJyIp2x9gti14Dk+J7y5tVEY/QuPT2b3idQrWL8NksWIPjSR92MnXsjmRmtJD1JYVEZGS49/mjIrD5nBSvncbibmXs2Xu4/4CFMcLCAolxJ1EVdFeIpJ9QTkmcyD7PvkHHzxyHcHuJP92R1gUGZdNZNVTk7AFBRMW39W/n+7X/obNs2ewe+Ub/gIUZ0NKZADje4dz56JCzCYT6a4Apv/LvKRb+kUyZcFBwgMtZEbbqT16R+eFdaUUHB3O1icukLTIAOZsqeS9XVVYzSYigyyM63X2Viy3WUyMyg7j2TW+MuZpLjvprgDGzysgJthCjtt3Yrdbzdz/HzE8+H6xv7jDlZmtL25X7fXQJy6wVcGHwUlBvLi+lPqmFq7oFsLNbxXQ1WXngYtafx79EwKZ8VEJg5KCsB19/YisMB5YWczyXdUMSAgk0OrbfkFyEHmFtdw0v4CYYCvZp9FGI4I7dSH54hvJe/52TCYLwQnpZF3f+reScskkNjwzCZsznLCkLP9xlL/weWoOFwAtRKT3IzgunX0fzKZ4g+84Cghxkfxz42uu1ZYdwltWRGjSsfkcga44rI5gKvdtI7b/peyY/4S/AMXxbEGhON3JeIr2EprkO5+5uuVycPUC1j42lqCYzv792kOjSBk2kQ2zJmN1BLcqgZ4+8jdsnzuD/R/O8RegOFPpqSlMvnk8N06ZisVsJjMjnT882Hq/UydP5LqbJxMZEU737Cx/mHly1p/Zd6CAlhYY2L8P3dLT+Mtrb/DukvewWq1EuyK5dcL4M27jtwJsNsaNuZo/PjULgG5d08jMSOfy0ePo5I6hdw/fOc5utzPjgencPf1BfwGKMaOuarWvlR/9k9x+fVoVpbhoyGCeevYF6uvrGT3iCkaOnUBmRleeeKR1EZTzB/bnvoce5aIhg/3rUV13zQjuuv8B3l28nMHnDSAw0HchdvGFQ1i3IY8rxtxIbCc3Pbtnt2njsQIUZy4+OY3hN0ziT/fcgslsISmtGxOntb5hMHLC7Tz663GEhEXQpVt3vLW+73PeSzMpLtgPtJDZO5fE1AyWzH2FNSsXYbHaCItwcfn1bReFPlVHig5SWlxIauaxm53RsQkEOoPJ376VQb+4iteeftRfgOJ4zpBQYjunULhvD126+W4K5vQfxIeL/s7vbhlBp4Rk/37DXdFceeMUZkwd7ytAkXpsSNHYO+7nlSf+m2XzXvUXoDjXIuNT6X3FzSz84xRMZjNRnTO4cFLrm9f9R01mwcM34wiJIKZLtj/ArP3bLCqLD0BLC/FZ/XF1TmfL4tfYtXoJZouVwDAXvYZPMNy2qsOFVB0pIia1u39baHQ8AUHBlOR/QdfBw1n16mP+AhTHsztDiYhLprzwa2K6+H7nid3PY/uHbzNv+rWEdepMTKrvGA0Kj6LviEm888hEXwGKzseuFwaNu5ePX36YrUtn+wtQnKku7nAm/CybX720ErPJRNe4iDbzkm79RQ8mPr+cCKedrMQoaup81wfPLtlEwZEqWoB+qW7SY8N5/eMvWZq3F6vFhCskkJt+lt3Ouxpjs1oYfX4G/7PQ1xuZHhdB17gIxsxchDssiB5JvptZdpuF/xo9kN+98Ym/uMPIga17eD/adoC+qe5WBR8uyErguaWbqW9s4qrcNG54egkZ8RE8fN2gVq/NTY/l939bwwVZCdiOvn7UeelMn/0JS/O+ZmBGLIEBvshzYXYCG/OLuH7mYtzhTnJOo40nYoqLi/vOsTpOp5NNW7/gqr+XftfT5ByaPzKcwQP7U15e3uaxoUOHctfvZ/LGzh+gYT9hgxLtVH62gP+8f5p/26bP1mBa/BBNlYd+wJbJydgumsof3ljJ3LnfvQbeuo2b+d/tFo7UnPlckZ+6AfF2Igo+4fbJbS92N2/eTKTDBO1MTJcOwGylwRHOxqK2w4el40kItVBY1cTqAz+OIYU/RmO7WQne9haNhzvukHUBR8YQXnpvC48/0bZ3+ye7zpSIiIiIiMiZOGmYamhowGYx8x1Ft+QcMgGOACter7fdx71eLwEnL4kvZ1mA2Tcm/nj1dfWYbG2r8EgHY3Wc8Hg6Xl1dHfazsDaVgMNioram/fkm9fV1+M500jG1YDbp+/l30dKCzlsdXGMzmCxntkakfP+aTFZqT3CtcNIwVV9fT2HxYQYlOs56w+T0DUywU1peQW1t+0Uy8vPzSYwIJCpInY7nitUMXcOa2LHjq1bbd+zciTX9gh+oVXIqzM5IHInZ7Nq166TP3blzB71jdKfiTJlNkBPZwo4vv2j38R1f7aDJ9IMVmpWTaW7CajYRHKAL9H8HNQ0tpIRbsevU1WGVeE1YE85tITI5TdYAWlzJJ7xWOOmcKYAePXow+815zM9vIb+8gXbWNZTvmcUEKRE2rk03M2Hc9eTl5Z3wuWPHjuW+Bx5ixf4WKr3NnHoBazldQTYTuTEt7Nz4CXfcNsVfFQvA7Xaz8B9vEVycR9OBzdCoMesdhwmzMwJL/7G8+Nc5PD3r5GuDuN1u3n5nIbvqQtlR1ky9pvSctiCbiVw3lO/ZwsQJN1LfTmlwt9vNooULiQ4PwUyT79a6dCwWG9idfF3RhLexRf8xHZzbaQZMrCvwUqvvq0OxmEzEOs0M6AR1hTug6MsfxfI6Px4mTAGBNHfux9KP1nLPb6fR0s5/0imFKYDs7Gx+OeV2EjonfecigfL9aG5upmD/Pl556QW2bt160ucPHz6cK0eNxu02VhpbTk1VVRUb1q3h2VnPtApS33K73dx7951kpKf6K2lJx3C4tJQly1fy5txTX3vC7XZz1z33kpaege24ympyajweD1vyNvD0zKfaDVLfcrvd/Pbee8noltGqgp10HE6nk/AIF1abDRMmmluaqa+rp+RwCQ0NKk7R0bhcLqwBdry1XkrLyn7o5shRzU1N7N27h1dfeZlRI66ie3amv7qmdAwVFRV8/OkaXnjhxXaDFJxGmBIREREREZFjNLFGRERERETEAIUpERERERERAxSmREREREREDFCYEhERERERMUBhSkRERERExACFKREREREREQMUpkRERERERAxQmBIRERERETFAYUpERERERMQAhSkREREREREDFKZEREREREQMUJgSERERERExQGFKRERERETEAIUpERERERERAxSmREREREREDFCYEhERERERMUBhSkRERERExACFKREREREREQMUpkRERERERAxQmBIRERERETFAYUpERERERMQAhSkREREREREDFKZEREREREQMUJgSERERERExQGFKRERERETEAIUpERERERERAxSmREREREREDFCYEhERERERMUBhSkRERERExACFKREREREREQMUpkRERERERAxQmBIRERERETFAYUpERERERMQAhSkREREREREDFKZEREREREQMUJgSERERERExQGFKRERERETEAIUpERERERERAxSmREREREREDFCYEhERERERMUBhSkRERERExACFKREREREREQMUpkRERERERAxQmBIRERERETFAYUpERERERMQAhSkREREREREDFKZEREREREQMUJgSEREREREx4P8Bx3CdYr1Gug0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCID-DPaanxU"
      },
      "source": [
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa']\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "teste=[]\n",
        "treino=[]\n",
        "for i in range(1, 15):\n",
        "  modelo_arvore = DecisionTreeClassifier(max_depth = i)\n",
        "  modelo_arvore.fit(x_treino, y_treino)\n",
        "  teste.append(modelo_arvore.score(x_teste, y_teste))\n",
        "  treino.append(modelo_arvore.score(x_treino, y_treino))"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "04jOVSHcckmR",
        "outputId": "af3b14a2-6a2f-4350-f208-b83470213ef4"
      },
      "source": [
        "sns.lineplot(x=range(1,15), y= teste, label='teste')\n",
        "sns.lineplot(x=range(1,15), y= treino, label='treino')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7a531dc8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EdsvV2nO1btDaO1vt1g3sXf7fhIrLKLv2TvwFqSuv5ULtx9MZtXfGxuergR0isjGtbQFwl4jEjTGzgKeMMWNFpKojnrCqqgbbzr4L3OfyhXpytfZcrRu09s6W2L6Buhfm4+s7iNBl3+GTWh/UHs6J2o+nvbX7fFaLP6gz2V11JzDIGOMHcG8Huu3NuQF4OL1BRCpFJO7ef8Gdd7zbvQMYljb50BaWrZRSbRYvX0/dC7/EVzyE/Cu/i5WXvaMM2aDVYBCRfcBGYI7bNAfYICLNDSMNBs4HHm3SPijt/hnAcEDcpseBr7p9o4Ezgefa+DqUUqpZ8a1/o37FfHwlI8i/4jtYYb3eeWsyHUq6GVhkjLkDqAbmARhjlgF3iMg6d7ovA0+LSHWT+e82xkwGkkAMuF5EKt2+nwILjTEfuv03iUhurtcppbJKfMtfqX/5N/j7jyZy2TexQhGvS8oJluNk39h8GwwHynUbQ8fL1dpztW7Q2jta/INXqX/5IfxlJhUKwXCz02Vj7ZnqgG0MI4BtTfv1yGelVLcTe/8VoqsW4h90KpFLv4YVaD4UVPM0GJRS3Urs3ZVEX12Mf8gEIrP+D1ZAD15rKw0GpVS3EXv7eaJrH8M/9Awis27F8ge9LiknaTAopbqF2KZnib7+BwLDJ5M34xYsv369tZe+c0qpnBfd8DSxN/5IYORZ5E2/CcunX20nQt89pVTOchyH2Po/EXvzKQKjppJ30VewfH6vy8p5GgxKqZzkOA6xN/5IbOMzBMacR94FN2D59NpjHUGDQSmVcxzHIfr6/xJ/61mCp1xI+PwvY1kaCh1Fg0EplVMcxyG69jHi77xA8NQZhKddp6HQwTQYlFI5w3FsomuWEH93JcHxlxCeOgfLsrwuq9vRYFBK5QTHsYmuXkj8/VUEJ1xO+OxrNBQ6iQaDUirrObZN/aqHSHywhtDEqwhN+byGQifSYFBKZTXHTlL/8m9IfPgaocmfIzz5s16X1O1pMCilspZjJ6hf+WsSH/2N0JlfIDzxSq9L6hE0GJRSWclJxKhbMZ/kjk2Ez/kioQmXe11Sj5FRMBhjxgCLgGKgCpgnIluaTLMYmJDWNAG4WkT+bIz5PvAlUhfiiQP/LiLL3fkWAjOBj935HheRu9r9ipRSOc+J1VK3/Bck93xA+Lx5hE6d7nVJPUqmawwLgPkissQYMxd4EGj0PyUi8xruG2NOB1YCy92mvwH3iUit2/eKMaZMROrc/ntE5Jcn8kKUUt2DXXeIumfvw67aRd70rxIcdY7XJfU4rR4VYowpBSYBS92mpcAkY0xJC7PdCDwqIlEAEVkuIrVu31uARWrtQymljrJrqqj7893Y1RVELv2ahoJHMjlccAiwW0SSAO5thdt+DGNMCLgWePg4y5sHbBWRXWlt3zLGvG2M+ZMxZmzG1Sulug37wB5qn7oLu+4gkSu+Q2Do6V6X1GN1xsbnq4EdIrKxaYcx5kLgx8CstObvAXtExDbGzAOeM8aMbAiiTLjXLs1KJSVFXpfQbrlae67WDT239uiej9jzzH/isywGXP9jwgNGdGBlreup7/vxZBIMO4FBxhi/iCSNMX5goNvenBtoZm3BGDMVWAJ8VkSkoV1EdqfdX2yM+TkwGNie6YuoqqrBtp1MJ+8yPfEi417L1bqh59ae2CPUPfdzrHABkdnf4ZD/JOjC96Envu8+n9XiD+pWh5JEZB+wEZjjNs0BNojI/qbTGmMGA+cDjzZpPxP4A/AFEXmzSd+gtPuXktpzaTdKqW4vsWMjdcvuxVfQl/zPfA9fnwFel6TIfCjpZmCRMeYOoJrUdgKMMcuAO0RknTvdl4GnRaS6yfy/AiLAg8aYhrbrReRtd7n9ARs4BHxGRBLtfUFKqdwQ3/JX6l9+CN9JQ4lc/i18ebk7nNPdZBQMIvI+cHYz7bObPG72+AMRObOFZc/MpAalVPcR27yC6Jol+MtOIXLp17FCEa9LUmn0yGelVJdxHIfYhqeJrXuSwLCJ5M24BSsQ8ros1YQGg1KqSziOTfS1PxB/ezmB0dPIu/AGvT5zltJgUEp1OsdOUr/qdyQ+eJXg+FnuBXb0qmvZSoNBKdWpnESM+pULSGx7k9DkzxGa9Bm9lkKW02BQSnUaJ1ZH3fP3k6x4j/C51xEaP6v1mZTnNBiUUp3Cqa+h9tn7sD/eTt7FNxEcfa7XJakMaTAopTqcfaSaumU/xT60j8gl/0Jg2ESvS1JtoMGglOpQ9sG91P7lJzjRI0Quv43AwFO8Lkm1kQaDUqrDJKt2ULfsXnAc8q+8HX/JcK9LUu2gwaCU6hCJyi3UPfczrGCEyBW34e8z0OuSVDtpMCilTljt1g3U/eWnWIX9yL/iO/gK9TpcuUyDQSl1QuJb/krlKw/j6zuIyOxv44v08rokdYI0GJRS7eLE66lf8yiJD1aTN/RUAtP/D1Yo3+uyVAfQYFBKtVlyfzl1KxfgHNxHaOJVlF06l48/qfO6LNVBNBiUUhlzHJvYpmeJvfEkVn5vIlf+K4GBp2D59aukO8nof9MYMwZYBBQDVcA8EdnSZJrFwIS0pgnA1SLyZ/dyoPcDlwEOcI+I/Nad77h9SqnsYR+ppv6lX5OseI/AiCnkXfCPWOECr8tSnSDTmF8AzBeRJcaYucCDwPT0CURkXsN9Y8zpwEpgudt0HTAKGE0qXDYYY1aIyLZW+pRSWSBevp76VQ9DMk7eBTcQMOfrifC6sVbPe2uMKQUmAUvdpqXAJGNMSQuz3Qg8KiJR9/EXgd+IiO1eK/pPwN9n0KeU8pATj1K/aiH1LzyAr6iEgs//iOApF2godHOZrDEMAXaLSBJARJLGmAq3fX/TiY0xIeBaIP2SnUOB7WmPd7jzt9anlPJI8uPt1K9cgH2gktDpswlN+bxuS+ghOuN/+Wpgh4hs7IRlN6u4uLCrnqrNSkpy9wLnuVp7rtYN2VG749gc/NszHF75KP78XpRdeweRERNanS8bam8vrb2xTIJhJzDIGON31xb8wEC3vTk3AA83adsBDAPecB+nryW01JeRqqoabNtpyyxdoqSkiP37D3tdRrvkau25WjdkR+127QHqX/4tyV3vEBg+ibwLbqAmr5CaVurKhtrbqyfW7vNZLf6gbjUYRGSfMWYjMAdY4t5ucLcHNGKMGQyc706T7nHgn4wxT5LawHy1O11rfUqpLpLYvpH6Vx7CiUcJn/dlgmMv0m0JPVSmQ0k3A4uMMXcA1cA8AGPMMuAOEVnnTvdl4GkRqW4y/yPA2UDDLq4/EpHyDPqUUp3MScSIvvYH4u++iK94CJHpt+DvqyfA68ksx8m+IZg2GA6U61BSx8vV2nO1bvCm9uQnO6l/cQF29W6Cp11K+KwvYPmDbV6Ovu/e6IChpBHAtqb9uouBUj2Q4zjEN68g+vofsEL5RGbfRmDweK/LUllCg0GpHsauO5TawLzzLfxDTyfvwhv1jKiqEQ0GpXqQxM63qH/5tzixWsLT5hI8dYZuYFbH0GBQqgdwknGirz9O/J3n8fUdTOSK7+LvN9jrslSW0mBQqpuzD1ZSt2I+dtVOguNmEj77GqxAyOuyVBbTYFCqG4tvW0/9S7/F8vmJXPYNAkPP8LoklQM0GJTqhhw7SeyNPxLbtAxfyQgiM2/FV3SS12WpHKHBoFQ3Y9cdov7F/yFZ8R7BsRcRPve6dh2boHouDQalupHk3g+pWzEfp76GvIu+QnDMeV6XpHKQBoNS3YDjOMTffZHo2qVYBf3I/+z/xX/SMK/LUjlKg0GpHOfEo9SvXkjiw7X4h55O5OKb9JKb6oRoMCiVw+yDldS98EvsT3YTmvJ5QhOvxLJavTCjUi3SYFAqRzXaFXX2t/VcR6rDaDAolWN0V1TV2TQYlMohuiuq6goZBYMxZgywiNQV1qqAeSKypZnprgG+D1iAA8wUkb3GmMVA+kVjJwBXi8ifjTF3Av8MVLh9a0Tk1na+HqW6rUa7ol54I0GjFzpUnSPTNYYFwHwRWWKMmQs8CExPn8AYMwW4E5guIpXGmN5AFEBE5qVNdzqwElieNvtiEbmt3a9CqW5Md0VVXa3VYDDGlAKTgFlu01Lgl8aYkibXff4mcK+IVAKIyMHjLPJG4FERiba/bKV6Bt0VVXkhkzWGIcBuEUkCiEjSGFPhtqcHw6lAuTFmFVAIPAncJSJHr7lpjAkB1wIzmzzHl4wxlwCVwA9EZG17X5BS3YXuiqq80pEbn/2kth3MAkLAc8AOYHHaNFcDO0RkY1rbAlIBEjfGzAKeMsaMFZGqTJ/YvXZpViopKfK6hHbL1dpztW74tPYj8jf2Pf0Als/HgC99j/yTJ3pcWeu6w/ueizqj9kyCYScwyBjjd9cW/MBAtz3dDuAJd4goaox5CjiLxsFwA/Bw+kwNQ0/u/ReMMTuB8cArmb6IqqoabNtpfcIu1hMvMu61XK0bUrXv23uA2LoniW38y9FdUY8UncSRLH9Nuf6+97TafT6rxR/Ura6Xisg+YCMwx22aA2xosn0B4DHgEmOMZYwJAjOATQ2dxpjBwPnAo+kzGWMGpd0/AxgOSGt1KdXdJI8cpG7ZvcQ2/oXgKReRf9W/6fEJyhOZDiXdDCwyxtwBVAPzAIwxy4A7RGQd8HtgCvAuYJPa6+ihtGV8GXhaRKqbLPtuY8xkIAnEgOvT1yKU6u4c2ya5YxO71i4hWXtId0VVnrMcJ/uGYNpgOFCuQ0kdL1drz6W6nfoa4rKa2LsrcQ7vJ9B3AKGLb8nJXVFz6X1vqifWnjaUNALY1rRfj3xWqoslP95OfPMK4h++Bsk4/jJD8OxrKJtyAR9/Uud1eUppMCjVFZxkgkT5OmKbV2Dv/RACIYJjphEcNwN/vyEAWH79OKrsoH+JSnUi+0g18fdeJv7eSzh1h7B69Sc8dQ7BMefpgWoqa2kwKNXBHMchWfkB8c0rSJSvB8fBP3QCoXEz8A8erwepqaynwaBUB3HiUeIfriW+eQX2J7sglE/wtEsInTodX69Sr8tTKmMaDEqdIPvgXmLvriQuqyBWh694COEL/pHgqHOwAmGvy1OqzTQYlGoHx7FJ7nyL2OaVJHe+BZafwMgpBMfNxN9/FJZleV2iUu2mwaBUGzj1NcQ/WE1sc+rYAyvSm9DkqwmOvQhffh+vy1OqQ2gwKJUB++BeYpueJb7lr5CM4R8whuBZXyAwfLLuZqq6Hf2LVqoFyU92EdvwDImPXgefn+Coc1PHHuTg0clKZUqDQalmJPd9RGzD0yS2b4BAmOBplxKacJkOF6keQYNBKZfjOCT3CLENT5PcvRlC+YQmfZbQ+FlYedl7zQ+lOpoGg+rxHMdJ7WG04RmSe7dgRXoROusaQqdejBWKeF2eUl1Og0H1WI5jkyhfT2zDM9hV27EK+hE+dy7BUy7ACoS8Lk8pz2gwqB7HsRMkPnyd2MZnsA/swerdn7wLbiAw+lzdw0gpNBhUD+IkYsQ/eJXYpmU4hz/G128wedNvJjDyLCyfnr9IqQYZBYMxZgywCCgGqoB5IrKlmemuAb4PWIADzBSRvcaYO4F/BircSdeIyK3uPPnA74DJQAK4TUSeOZEXpVQ6Jx4l/t5LxN56Dqf2AL6SkeRNvQ7/sNP1hHZKNSPTNYYFwHwRWWKMmQs8CExPn8AYMwW4E5guIpXGmN5ANG2SxSJyWzPLvg04JCKjjDGjgdXGmFEiUtPWF6NUOid6hNjmF4m//TxOtAb/wLGELr4J/8CxesoKpVrQajAYY0qBScAst2kp8EtjTImI7E+b9JvAvQ3XaxaRgxnW8EVS14NGRLYYY9YBlwOPZzi/Uo3YdYeIv/08sc0vQrwO/9DTCU+8Cn//UV6XplROyGSNYQiwW0SSACKSNMZUuO3pwXAqUG6MWQUUAk8Cd4lIw8WYv2SMuQSoBH4gImvd9qHA9rTl7HCXnTH32qVZqaSkyOsS2i2Xancch/j+nXz8/OPUbngBJxGnYOw59Dn37wgPGOF1eRnLpfe8Ka3dG51Re0dufPYDE0itWYSA50h9yS8mNRR1l4jEjTGzgKeMMWNFpKojnriqqgbbdlqfsIv1xIuMdyXHsbH3l5MoX0+8fD3Oob1g+QiMnsOO0qUAABXHSURBVErojCvw9RnIIYAsfx0NcuE9Px6t3Rvtrd3ns1r8QZ1JMOwEBhlj/O7agh8Y6Lan2wE8ISJRIGqMeQo4i9S2hcqGiUTkBWPMTmA88Io73zA+XfsYCryU0atTPY5jJ0nuERLl60lsW49TewAsP/5BYwlMuIz+k8+nuk53tlPqRLT6CRKRfcaYjcAcYIl7u6HJ9gWAx4DZxphH3OXOAJ4AMMYMEpHd7v0zgOGAuPM9DnwVWOdufD7TfQ6lgNRupsndm4mXr0+duyh6BPwhAkNOIzB8EoFhZxy9fnKgsAjqcvPXn1LZItOfVjcDi4wxdwDVwDwAY8wy4A4RWQf8HpgCvAvYwHLgIXf+u40xk4EkEAOuT1uL+Cmw0Bjzodt/k4joJ7uHc2J1JHZsIrHtTRI734J4PYQiBIaeQWDEZAKDT8MK6tXRlOoMluNk39h8GwwHynUbQ8fzona7/jDJbRuIb1tPctdmsBNYkV4Ehk0iMGJyajfTVo5M1vfcG1q7NzpgG8MIYFvTfh2MVZ6ya6pSawXl60lWCjgOVmExwXEzUmFQOkqPSlaqi2kwqC5nH6gkvm0difL12PvLAfD1HUjojCsJjJiCr3ioHoCmlIc0GFSnchIx7KodJPeXk9y3leS+j3AO7QPAVzKC0FlfIDh8Mr4+ZR5XqpRqoMGgOozj2DgH95Lc91Hq3/6PsKt2gJ0EwMrvg7/0ZPzjZxEYPglfYbHHFSulmqPBoNrNrjuE7a4FpIKgHGK1qc5gHv6ThhM67VJ8pSPxl56Mr6CvtwUrpTKiwaAy4iSiJD/ejt0QAvu24tS4B65bPnz9BhMceRb+0pH4Skfi6zNQNxorlaM0GNQxHNsmtn8H8fffJrk/FQT2J7vAsQGwCovdIaGZ+EpPxl88TI8pUKob0WBQRzl2kvg7K4hu+DM10SOpxlAEf8lIQmdckVobKBmJL7+3t4UqpTqVBoMCILnvI+pXL8Su2oF/yGn0m3gRtXkDsXr314vZKNXDaDD0cE70CNE3/kj83Zew8nuTN/NWAiOmUFTai/ocPRpUKXViNBh6KMdxSGx9nejapTj1hwiOn0l4yuexQhGvS1NKeUyDoQeyD+6l/tXFJHdvxlcygshl38RfMtzrspRSWUKDoQdxknFim5YR2/A0+AKEz51L8NTpulupUqoRDYYeIlHxHtHVi7APVhIYeRbhqXP0gDOlVLM0GLo5u+4Q0df+QGLLGqyiEiKXf4vAkAlel6WUymIZBYMxZgywCCgGqoB5IrKlmemuAb4PWIADzBSRvcaY7wNfInUhnjjw7yKy3J1nITAT+NhdzOMicteJvCiVOm9RXFYTff1/IV5PaOJVhCZehRUIeV2aUirLZbrGsACYLyJLjDFzgQeB6ekTGGOmAHcC00Wk0hjTG4i63X8D7hORWmPM6cArxpgyEalz++8RkV+e6ItRKclPdhFdvYjk3i34ywzh8+bh7zvI67KUUjmi1WAwxpQCk4BZbtNS4JfGmJIm133+JnBvwyU7ReRgQ0fD2oHrLVJrFMXArhMrX6Vz4lFibz5F7K3lWKEIeRfeSGDMeXptA6VUm2SyxjAE2C0iSQARSRpjKtz29GA4FSg3xqwCCoEngbtEpOk1N+cBW0UkPRS+ZYz5KrAV+DcRea99L6fnSmzfSP2aR3Bqqgia8wmdfQ2+vCKvy1JK5aCO3PjsByaQWrMIAc8BO4DFDRMYYy4Efsynax8A3wP2iIhtjJkHPGeMGdkQRJlwr12alUpKOvfLOXGoio+ff4g6eZ3gSYM56XM/IjJ0XIcsu7Nr7yy5Wjdo7V7R2hvLJBh2AoOMMX53bcEPDHTb0+0AnhCRKBA1xjwFnIUbDMaYqcAS4LMiIg0zicjutPuLjTE/BwYD2zN9EVVVNdh20xUT73XmRcYdO0l88wqi6/4f2ElCZ36B0ITLqPEHqOmA58zVC6Tnat2gtXulJ9bu81kt/qBuNRhEZJ8xZiMwh9QX+xxgQ5PtCwCPAbONMY+4y50BPAFgjDkT+APwBRF5M30mY8yghnAwxlxKas+l3ajjSp3wbhF21Xb8QyaQN20uvl6lHbb8/QfqqE04VH1yBMdxcBywHQfbve84Draddp+GNo5O76RNbzdpywsF6FsUpk9hmN4FIXw+3QaiVDbJdCjpZmCRMeYOoJrUdgKMMcuAO0RkHfB7YArwLmADy4GH3Pl/BUSAB40xDcu8XkTedpfb353nEPAZEUmc6Avrjuz6w8T+9jjx91c3OuFdR2xcjids3nh/Ly+u30X5nq779WRZ0LsgdDQo+hSF6VsYPuZxJOzv1I3o8USSw7VxauriHK6LU9NwvzZGTV3D/TiO4+D3+/D7LPw+C597m/rna/TY57Pw+4/fFzg6f2p5JxUfxkkk6VUQold+kEg4oDsOKE9YjpN9QzBtMBwo7+5DSY5tE3//FaJvPAGxOoKnXUJ40mc75IR31YejvLRhN6s27uZQbZyy4nwuPH0gwwb34fChenw+C8sCy7LwWeCzLCyrcVvqNtV2dHrSH386nWVBXTTBgcMxqmuiVB+OcuBwlAM1UaprUveP1B/7uyAc9NOn0A0QNzSOBkhRmD6FIfoUhikb0Js9lQc5Up+gxv1SP1zb8GUfS926X/zpARCNH3+TVkFegML8EIWRAD7LwrYdEnZqrSnZ8C9pYzsOyWTqcaM+26Y9H7OA36IoP0Sv/BBFBUF654coKkg97lUQTLXnh+hVEKIoP0jA792pTXricEw26IChpBHAtqb9euRzlkvu+4j6Vxdjf7wNf9kphKddj7/fiR2T4DgOW3Yd5MX1u3jzg/3YtsPpo05ixuTBnDq8L5Zldf6HZcDxu2LxZCooDkc5UBNzb6NHbz/cdZADNTESSfuYeSPhAHXR469w5oX8FEaCFOWnvlgHFhdQlJ96XBgJUhgJfXo/P0hBXgB/B5xLynbSwiLpuCFiNwqRSEGYHRUHOHwkzqHaGIeOxDhUG+NwbZxDR2JUfHyEQ0fizb5uSAVYKkiCqbBoCBH3cZ+iMP2K8uhdGMKnayKqBRoMWeqYYaPpNxM4+ewTGlqIxZO8/m5quGjHvhrywwFmTB7M9MmDKe2TPafbDgX9lPbNp7Rv/nGncRyHI/UJqg9/GhgHDkexLQuf41DofrkXRYLur/3U42DAm1/VPsvC57cI+IFg89OUlBTRK+xvcTmO41AfS34aHEdSw12fBkmcw0di7P74CIe2Vze79uX3WfQpDNOvV5h+vfLoV5Ra8+rXKy/VVpRHUX5Qh7F6MA2GLJMaNnqZ6Bt/hFg9wQmXnvCwUdXBelZu2MXqTXuoqYsz6KQC5l1qmDpuAOFQy19E2cqyrKNf9kNKP927IpeHBTJhWRaRcIBIOED/FoKzQSJpU1MX52CNO3R3qJ5PDkf55FA9nxyK8lHFQdYfjpJINh7rCvh99CtKhUfforxmQ6QgT7eBdFcaDFmkI4eNHMdBdhxIDRdtSe1ANnF0CTMmD+aUoX30A91DBPy+1Eb8wjDDaH5/d9txOFwbPxoWnxyup9q9/eRQlA92VlN9OIbdZENJKOijnxsaIwb1YWDfCCMH9qK0b0T/vnKcBkMW6Mhho2gsydrNlbz45i527z9CQV6Ay84eysUTB3FS7+wZLlLZw2dZ9C4I0bsgxIiy5qexbYeDR2Kp8Ehb42gIjxff2EF9LLUBvzASZOTAXows68XIQanb/LzjjJ+prKTB4KGOHDbad6COlet38epbe6iNJhhaWsg/Xn4KZ5/an1AwN4eLVPbw+Sz6usNIJzfT36+4kE3vVfJRxUG2Vhzio4pDvL21ioZ1jLLifEYO7MXJA3szcmAvBpUUdMhGfdU5NBg8kty3lfpXH0kNGw0cS3ja3DafAdVxHDZv+4QX1+3ira1VWJbFZJMaLho9uLeuzqsu4/dZDCktZEhpIReekfo7rq1PUF6ZComPdh9k04dVrHm7EkgNQ40Y0Cu1ZuGGRd+isJcvQaXRYOhinw4brcLK79PmYSPHcThQE+PND/az8s1d7KmqpSg/yBXnDufiiYP0w6WyRn5egHHD+zFueD8g9be7/2A9H+3+dK3i+Td2krR3ANCvVzgVEmW9OHlQL4b1L9K1XY9oMHSRY4eNLmtx2CiRtNlbXUdl1RH2VNWyp6qWyk9S9xvGckeUFfGVK8dy5in9PdsNU6lMWZZFaZ8IpX0inDMudSBLPJFkx94aNygO8lHFIda9vw9IrYUMLi1k9ODezJg8OKO9sFTH0GDoAi0NGx2pj7tf/Kkv/Ur3/v4D9Y32AulbFKasOJ9p48sY4I7Xjijr5dVLUqpDBAN+Th7Um5MH9SZ1Jn84eCR2NCS27j7IyxsqWLl+N1PH9+eqaSOy6pib7kqDoRMlaw9Rv+p3xN9fhRPpzf7xc9nqH82e1w+zp+pNKquOcKg2fnT6gN+if798hpQWcubY/pQV51NWnE//vvlEwvpfpXqG3gUhJo4uYeLoEgAO1kRZ9toOXt64m7Xv7OXc0wZw1bnDKdGA6DQ99tvm4IFDvL96Bdidc76+QKKW4dWv4bdjrIqO59lPTiO62wdspTASpKw4nzNGn8SAfgVHA+Ck3hE906hSTfQuDDNn5mguP2coy9Zu5+WNFax9p5Jppw3gyqnDOUkDosP12GCo3vIWp+75c6c+x3YGsr7XTCKlQ5njfvkP6JdPUX6oU59Xqe6oT2GYa2eN4fJzhrFs7XZe2bSbNW9Xct6EMq6cOpzi3nlel9ht9NhgGH7medinnQ52xheKaxvLx0VDBjL+45rOWb5SPVTfojDXXTKGy88Zyl9e286qjRW8+tYezj99IFdOHUa/XhoQJ6rHBgPQ6ddE1uMIlOo8/Xrlcf0lhtlnD+Mva7exelMFr75VwfmnD+SKczQgTkRGwWCMGQMsAoqBKmCeiGxpZrprgO8DFuAAM0Vkr3s50PuBy9z2e0Tkt+48x+1TSqnWFPfOY95lpzB76jCe+WtqDWL1pgouPH0Qs6cO02N72iHTnd8XAPNFZAwwH3iw6QTGmCnAncAsERkPnAccdLuvA0YBo4GpwJ3GmOEZ9CmlVEZO6h3hHy4/hbtvOoep4wbw0obd/OuCtTz2wgccqIl6XV5OaTUYjDGlwCRgqdu0FJhkjClpMuk3gXtFpBJARA6KSL3b90XgNyJiu9eK/hPw9xn0KaVUm5T0ifCPs8dy91fP4ZxT+7PyzVRALF2xhYMaEBnJZChpCLBbRJIAIpI0xlS47fvTpjsVKDfGrAIKgSeBu0TEAYYC29Om3UHD0Swt9ymlVLuU9olwwxVjueLcYTyzZhsr1u/klY27uWjiIC4/Zxi9C3TvwOPpyI3PfmACMAsIAc+R+pJf3IHP0Sz32qVZqaSkczdwd6ZcrT1X6watvTOUlBQxfkx/KvbX8PsXhBXrdvLKpgquOHcEn7941NFpclVn1J5JMOwEBhlj/O7agh8Y6Lan2wE8ISJRIGqMeQo4i1Qw7ACGAW+406avJbTUl5Gqqhpsux1XW+9kuXw1sVytPVfrBq29swWB62eNYeakQTz91238v5c/5C9ryjHD+hLwWeS7V8bLz3Nvm9yP5Lm3YX/WnDK8ve+7z2e1+IO61WAQkX3GmI3AHGCJe7vB3R6Q7jFgtjHmEXe5M4An3L7HgX8yxjxJas+mq4HzM+hTSqkOVVZcwE1XjeOqc4fz7Os7qDoUpepIlNr6BLXRBNFY68c2hUP+VHCkBUZzwZIX9pMXDBAO+giF/ISDaf9CfkIBX1bu1p7pUNLNwCJjzB1ANTAPwBizDLhDRNYBvwemAO8CNrAceMid/xHgbKBhF9cfiUh5Bn1KKdUpyooLuGH22GN+dSdtm7pokrpo4mhYNNyvizZ+3HD/YE2Myqpaat32ppdBPR4LCAX9qeBww6JpeBztC/rJC/mP3i+MBJnZr6BT3hvLyfAFZKnhQLkOJXW8XK09V+sGrd0rHV274zjE4vbR0IjGk8TiSepjSaLxpPvYTt2PJY/2p/psorFE6jaePn2SaMw+JnB+dNNUBvdr+7mi0oaSRgDbmvb36COflVKqo1mWlfqlH/J36MF1juOQSDpHg8J2HMaOKu2UQNZgUEqpHGBZFsGAlbooVyTYqc+VHZvWlVJKZQ0NBqWUUo1oMCillGpEg0EppVQjGgxKKaUa0WBQSinVSK7vruqH1MEa2Sqba2tNrtaeq3WD1u6VnlZ72jz+5vpz/cjn84DVXhehlFI56nzg1aaNuR4MYeBMYA/Q+pmvlFJKQWpNoYzUWa2PuXpRrgeDUkqpDqYbn5VSSjWiwaCUUqoRDQallFKNaDAopZRqRINBKaVUIxoMSimlGtFgUEop1UiunxIj6xhjioFHgJOBGLAF+KqI7Pe0sDYyxvwAuBM4TUTe8bicjBhj8oCfAzOBemCtiNzkbVWtM8ZcCfyY1LXhLeCHIvKkt1U1zxhzL/B3pK63fvRvwxgzBlgEFANVwDwR2eJVnc1prvZc+bwe731P6+/Qz6uuMXQ8B/iJiBgROQ3YCtzjcU1tYoyZBJwDbPe6ljb6CalAGOO+99/3uJ5WGWMsUl9M14vIGcD1wCJjTLZ+Nv8EXMCxfxsLgPkiMgaYDzzY1YVloLnac+Xzerz3vVM+r9n6x5ezROQTEXk5rek1YJhH5bSZMSZM6oN9i9e1tIUxphCYB3xfRBwAEdnrbVUZs4He7v0+wB4RsT2s57hE5FUR2ZneZowpBSYBS92mpcAkY0xJV9fXkuZqz5XPa3O1Q+d9XjUYOpH7q+8W4M9e19IGPwKWiMg2rwtpo5NJDWH8wBizzhjzsjHmPK+Lao0bYtcATxljtpP6ZTjP26rabAiwW0SSAO5thdueM/Tz+ikNhs71AFAD/NLrQjJhjJkKTAF+5XUt7eAHRgIbRGQK8K/Ak8aYXt6W1TJjTAD4N+CzIjIMuAr4X3cNSHUt/by6NBg6ibuxaDTwxWwdFmjGhcBYoNwYsw0YDCw3xlziZVEZ2gEkcIczROR14GNgjJdFZeAMYKCIrAFwb4+Q+n/IFTuBQcYYP4B7O9Btzwn6eW1Mg6ETGGPuBiYDV4vIMae0zVYico+IDBSR4SIyHNgFXCoiz3tcWqtE5GPgJWAWHN1LphT40Mu6MrALGGyMMQDGmLFAf1IbQXOCiOwDNgJz3KY5pNbcsmrPnuPRz+ux9LTbHcwYMw54B/gAqHOby0Xkc95V1T7ur5Arc2h31ZHAw6R2mYwD3xORZ72tqnXGmOuA20lthAb4gYj8ycOSjssYcz/weWAAqTWyKhEZZ4w5hdTuqn2BalK7q4p3lR6rudpJbd/J+s/r8d73JtNso4M+rxoMSimlGtGhJKWUUo1oMCillGpEg0EppVQjGgxKKaUa0WBQSinViAaDUkqpRjQYlFJKNaLBoJRSqpH/D6frWhHQIrsbAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "2aws-huBeuSR",
        "outputId": "93b711d6-3fa3-4ffb-f77d-176382bc1962"
      },
      "source": [
        "pd.get_dummies(dados_combinados['tratamento'])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       com_controle  com_droga\n",
              "0                 0          1\n",
              "1                 0          1\n",
              "2                 0          1\n",
              "3                 0          1\n",
              "4                 0          1\n",
              "...             ...        ...\n",
              "23809             0          1\n",
              "23810             0          1\n",
              "23811             1          0\n",
              "23812             0          1\n",
              "23813             0          1\n",
              "\n",
              "[23814 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "7fDrcG4Ee4T0",
        "outputId": "80cfb674-3e87-485d-cd9d-1279438652df"
      },
      "source": [
        "pd.get_dummies(dados_combinados, columns=['dose', 'tratamento', 'tempo'])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>composto</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>g-35</th>\n",
              "      <th>g-36</th>\n",
              "      <th>g-37</th>\n",
              "      <th>...</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>dose_D1</th>\n",
              "      <th>dose_D2</th>\n",
              "      <th>tratamento_com_controle</th>\n",
              "      <th>tratamento_com_droga</th>\n",
              "      <th>tempo_24</th>\n",
              "      <th>tempo_48</th>\n",
              "      <th>tempo_72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>0.2198</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.5055</td>\n",
              "      <td>...</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>-0.2102</td>\n",
              "      <td>0.1656</td>\n",
              "      <td>0.5300</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>-1.1800</td>\n",
              "      <td>0.1256</td>\n",
              "      <td>-0.1219</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>-1.6740</td>\n",
              "      <td>-1.6720</td>\n",
              "      <td>-1.2690</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>-0.7110</td>\n",
              "      <td>-1.4470</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>id_fffb1ceed</td>\n",
              "      <td>df1d0a5a1</td>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>-0.1736</td>\n",
              "      <td>0.3686</td>\n",
              "      <td>-0.1565</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>id_fffb70c0c</td>\n",
              "      <td>ecf3b6b74</td>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>0.4626</td>\n",
              "      <td>0.0819</td>\n",
              "      <td>0.1586</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>id_fffc1c3f4</td>\n",
              "      <td>cacb2b860</td>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>-0.2355</td>\n",
              "      <td>-0.5222</td>\n",
              "      <td>-1.1470</td>\n",
              "      <td>...</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>id_fffcb9e7c</td>\n",
              "      <td>8b87a7a83</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>1.8480</td>\n",
              "      <td>-2.7180</td>\n",
              "      <td>-5.3160</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>id_ffffdd77b</td>\n",
              "      <td>972f41291</td>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>-0.5768</td>\n",
              "      <td>0.5145</td>\n",
              "      <td>-0.9913</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 883 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id   composto     g-0  ...  tempo_24  tempo_48  tempo_72\n",
              "0      id_000644bb2  b68db1d53  1.0620  ...         1         0         0\n",
              "1      id_000779bfc  df89a8e5a  0.0743  ...         0         0         1\n",
              "2      id_000a6266a  18bb41b2c  0.6280  ...         0         1         0\n",
              "3      id_0015fd391  8c7f86626 -0.5138  ...         0         1         0\n",
              "4      id_001626bd3  7cbed3131 -0.3254  ...         0         0         1\n",
              "...             ...        ...     ...  ...       ...       ...       ...\n",
              "23809  id_fffb1ceed  df1d0a5a1  0.1394  ...         1         0         0\n",
              "23810  id_fffb70c0c  ecf3b6b74 -1.3260  ...         1         0         0\n",
              "23811  id_fffc1c3f4  cacb2b860  0.3942  ...         0         1         0\n",
              "23812  id_fffcb9e7c  8b87a7a83  0.6660  ...         1         0         0\n",
              "23813  id_ffffdd77b  972f41291 -0.8598  ...         0         0         1\n",
              "\n",
              "[23814 rows x 883 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcz8hBLedsZq",
        "outputId": "353b92d5-e84c-460f-e7e9-4489a127eb56"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "\n",
        "x = dados_combinados.drop(['id', 'n_moa', 'ativo_moa', 'composto'], axis=1)\n",
        "x = pd.get_dummies(x, columns=['dose', 'tratamento', 'tempo'])\n",
        "y = dados_combinados['ativo_moa']\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "\n",
        "modelo_randomforest = RandomForestClassifier()\n",
        "modelo_randomforest.fit(x_treino, y_treino)\n",
        "modelo_randomforest.score(x_teste, y_teste)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6857022884736511"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vuxo1Yi2g7hi"
      },
      "source": [
        "# Desafios Aula 05\n",
        "\n",
        "###Desafio 01: Tente outros modelos ou mude os parâmetros dos modelos que testamos em aula.\n",
        "\n",
        "###Desafio 02: Estude a documentação do Sklearn.\n",
        "\n",
        "###Desafio 03: Choosing the right estimator\n",
        "\n",
        "###Desafio 04: Teste outros problemas (perguntas). 0, 1 ou +.\n",
        "\n",
        "###Desafio 05: Escolha a droga mais utilizada e tente criar um modelo para prever esta label.\n",
        "\n",
        "###Desafio 06: Resumo do que você aprendeu com os dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b3K6xjv8jXMB"
      },
      "source": [
        "#Desafio 01: Tente outros modelos ou mude os parâmetros dos modelos que testamos em aula."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dmgFLTIk3uo",
        "outputId": "5a6102ce-431e-43c7-b93c-d0fceab8b7e0"
      },
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa']\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)\n",
        "modelo_classificador = SGDClassifier(loss=\"hinge\", penalty=\"l2\")\n",
        "modelo_classificador.fit(x_treino, y_treino)\n",
        "modelo_classificador.predict(x_teste)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False,  True,  True, ...,  True,  True, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbCb0ekNjZ_q"
      },
      "source": [
        "\n",
        "#Desafio 02: Estude a documentação do Sklearn.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHxv0736jdOy"
      },
      "source": [
        "\n",
        "#Desafio 03: Choosing the right estimator - Estudar esse diagrama do Sklearn.\n",
        "\n",
        "https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-4En82Njfpo"
      },
      "source": [
        "\n",
        "#Desafio 04: Teste outros problemas (perguntas). 0, 1 ou +."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mangImLIMnAd"
      },
      "source": [
        "x_dose = dados_combinados.loc[:, 'g-10':'c-99']\n",
        "y_dose = dados_combinados['dose'].map({'D1':0, 'D2':1})"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jhx2bUTHNKrN",
        "outputId": "43db7824-cff7-4951-8d26-38fbc351a138"
      },
      "source": [
        "x_treino3, x_teste3, y_treino3, y_teste3 = train_test_split(x_dose, y_dose, test_size = 0.2, stratify=y, random_state=376)\n",
        "modelo_rlogistica2 = LogisticRegression(solver='newton-cg')\n",
        "modelo_rlogistica2.fit(x_treino3, y_treino3)\n",
        "modelo_rlogistica2.score(x_teste3, y_teste3)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9338652110014697"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8wHT5dYjh8H"
      },
      "source": [
        "\n",
        "#Desafio 05: Escolha a droga mais utilizada e tente criar um modelo para prever esta label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U89Lw3mDNy-2",
        "outputId": "30319c95-f125-4776-f32f-f286a1bf7e0e"
      },
      "source": [
        "dados_combinados['composto'].value_counts()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cacb2b860    1866\n",
              "87d714366     718\n",
              "9f80f3f77     246\n",
              "8b87a7a83     203\n",
              "5628cb3ee     202\n",
              "             ... \n",
              "23458e56a       1\n",
              "58d311643       1\n",
              "00dba5599       1\n",
              "eff33f208       1\n",
              "828099693       1\n",
              "Name: composto, Length: 3289, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "DATWi9vTbczX",
        "outputId": "08f8580b-dbae-4d55-cf0c-6e728fa1b773"
      },
      "source": [
        "dados_combinados.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>tratamento</th>\n",
              "      <th>tempo</th>\n",
              "      <th>dose</th>\n",
              "      <th>composto</th>\n",
              "      <th>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
              "      <th>g-23</th>\n",
              "      <th>g-24</th>\n",
              "      <th>g-25</th>\n",
              "      <th>g-26</th>\n",
              "      <th>g-27</th>\n",
              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>...</th>\n",
              "      <th>c-62</th>\n",
              "      <th>c-63</th>\n",
              "      <th>c-64</th>\n",
              "      <th>c-65</th>\n",
              "      <th>c-66</th>\n",
              "      <th>c-67</th>\n",
              "      <th>c-68</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id_000644bb2</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>24</td>\n",
              "      <td>D1</td>\n",
              "      <td>b68db1d53</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
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              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3680</td>\n",
              "      <td>0.8427</td>\n",
              "      <td>0.1042</td>\n",
              "      <td>0.1403</td>\n",
              "      <td>0.1758</td>\n",
              "      <td>1.2570</td>\n",
              "      <td>-0.5979</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
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              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id_000779bfc</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D1</td>\n",
              "      <td>df89a8e5a</td>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
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              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>...</td>\n",
              "      <td>0.3905</td>\n",
              "      <td>0.7099</td>\n",
              "      <td>0.2912</td>\n",
              "      <td>0.4151</td>\n",
              "      <td>-0.2840</td>\n",
              "      <td>-0.3104</td>\n",
              "      <td>-0.6373</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id_000a6266a</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>18bb41b2c</td>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0444</td>\n",
              "      <td>0.1894</td>\n",
              "      <td>-0.0014</td>\n",
              "      <td>-2.3640</td>\n",
              "      <td>-0.4682</td>\n",
              "      <td>0.1210</td>\n",
              "      <td>-0.5177</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id_0015fd391</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>48</td>\n",
              "      <td>D1</td>\n",
              "      <td>8c7f86626</td>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 879 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-98    c-99  n_moa  ativo_moa\n",
              "0  id_000644bb2  com_droga     24   D1  ...  0.3801  0.4176      1       True\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.6077  0.7371      0      False\n",
              "2  id_000a6266a  com_droga     48   D1  ... -1.4080  0.6931      3       True\n",
              "3  id_0015fd391  com_droga     48   D1  ... -0.3876 -0.8154      0      False\n",
              "4  id_001626bd3  com_droga     72   D2  ... -0.3786  0.7125      1       True\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlXii1eOONbn"
      },
      "source": [
        "x_composto = dados_combinados.loc[:, 'g-0':'c-99']\n",
        "y_composto = dados_combinados['composto'] == 'cacb2b860'"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGPLwDPlPduW"
      },
      "source": [
        "x_treino_c, x_teste_c, y_treino_c, y_teste_c = train_test_split(x_composto, y_composto, test_size = 0.2, stratify=y)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x7eeNG8lebVR",
        "outputId": "b8fc0849-f829-4275-c057-fb884cc3694b"
      },
      "source": [
        "modelo_rlogistica3 = LogisticRegression(solver='newton-cg')\n",
        "modelo_rlogistica3.fit(x_treino_c, y_treino_c)\n",
        "modelo_rlogistica3.score(x_teste_c, y_teste_c)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.948141927356708"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0twRi-g-fOfA",
        "outputId": "6e1fdb32-f466-445a-8b15-e8c3e7f58b58"
      },
      "source": [
        "# Teste Dummy\n",
        "modelo_dummy_c = DummyClassifier('most_frequent')\n",
        "modelo_dummy_c.fit(x_treino_c, y_treino_c)\n",
        "previsao_dummy_c = modelo_dummy.predict(x_teste_c)\n",
        "accuracy_score(y_teste_c, previsao_dummy_c)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08712996010917488"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dybfLtmxftr6"
      },
      "source": [
        "Dado o resultado do Teste Dummy em comparação ao resultado da regressão logística fica comprovado que o método escolhido é relvante para a análise de recuperação da marcação de composto."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jL6D-xq7jkzj"
      },
      "source": [
        "#Desafio 06: Resumo do que você aprendeu com os dados.\n",
        "\n",
        "Aprendemos a utilizar machine learning para estudar a base de dados e retirar insights da base de forma automatiza utilizado as grandes ferramentas disponíveis no mercado."
      ]
    }
  ]
}